diff --git a/.gitattributes b/.gitattributes index 10b20da09f3316d87528b5df4e494b536902ae82..01a9ee3840eff2cc245add4801e7244d3846ce67 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1338,3 +1338,7 @@ P63/Spatiallm1_MasterSlam1/room_with_scan_preview.glb filter=lfs diff=lfs merge= P64/Spatiallm1_MasterSlam1/room_with_scan_preview.glb filter=lfs diff=lfs merge=lfs -text I87X/Spatiallm1_MasterSlam1/room_with_scan_preview.glb filter=lfs diff=lfs merge=lfs -text I87X/Spatiallm1_MasterSlam2/room_with_scan_preview.glb filter=lfs diff=lfs merge=lfs -text +I12X/MasterSlam1_ScanVideo1/scan.ply filter=lfs diff=lfs merge=lfs -text +I12X/MasterSlam2_ScanVideo1/scan.ply filter=lfs diff=lfs merge=lfs -text +I12X/MasterSlam3_ScanVideo2/scan.ply filter=lfs diff=lfs merge=lfs -text +I12X/MasterSlam4_ScanVideo2/scan.ply filter=lfs diff=lfs merge=lfs -text diff --git a/I12X/MasterSlam1_ScanVideo1/compare_t3.py b/I12X/MasterSlam1_ScanVideo1/compare_t3.py new file mode 100644 index 0000000000000000000000000000000000000000..a818a95f2ea26ba9c5daad7ccbc908add03d8c57 --- /dev/null +++ b/I12X/MasterSlam1_ScanVideo1/compare_t3.py @@ -0,0 +1,68 @@ +import json, sys +import numpy as np +from scipy.spatial.transform import Rotation as R +from scipy.spatial import cKDTree + +root, video, names = sys.argv[1], sys.argv[2], sys.argv[3:] # room dir, T3 capture name, MasterSlam folders +gt = {} +for line in open(f"{root}/ScanVideos/{video}/poses.jsonl"): + d = json.loads(line) + if d["state"] == "TRACKING": + gt[d["i"]] = (np.array(d["p"]), np.array(d["q"])) # q = x,y,z,w (ARCore) + +def umeyama(src, dst): + mu_s, mu_d = src.mean(0), dst.mean(0) + xs, xd = src - mu_s, dst - mu_d + U, D, Vt = np.linalg.svd(xd.T @ xs / len(src)) + S = np.eye(3); S[2, 2] = np.sign(np.linalg.det(U @ Vt)) + Rm = U @ S @ Vt + s = np.trace(np.diag(D) @ S) / xs.var(0).sum() + return s, Rm, mu_d - s * Rm @ mu_s + +res = {} +C = R.from_matrix(np.diag([1.0, -1.0, -1.0])) # OpenGL (ARCore) camera -> OpenCV camera +for name in names: + T = np.loadtxt(f"{root}/{name}/scan.txt") + idx = np.round(T[:, 0] * 30).astype(int) + keep = [k for k, i in enumerate(idx) if i in gt] + P = T[keep, 1:4]; Q = T[keep, 4:8] + G = np.array([gt[idx[k]][0] for k in keep]); GQ = np.array([gt[idx[k]][1] for k in keep]) + s, Rm, t = umeyama(P, G) + Pa = (s * (Rm @ P.T)).T + t + ate = np.linalg.norm(Pa - G, axis=1) + # relative rotation error between consecutive keyframes (alignment-free) + rre = [] + for a, b in zip(range(len(keep) - 1), range(1, len(keep))): + de = (R.from_quat(Q[a]).inv() * R.from_quat(Q[b])) + dg = C.inv() * R.from_quat(GQ[a]).inv() * R.from_quat(GQ[b]) * C + rre.append(np.degrees((de.inv() * dg).magnitude())) + path_gt = np.linalg.norm(np.diff(G, axis=0), axis=1).sum() + print(f"{name}: {len(keep)} kf matched | Sim3 scale {s:.3f} | ATE rmse {np.sqrt((ate**2).mean())*100:.1f} cm " + f"(max {ate.max()*100:.1f}) | RPE-rot mean {np.mean(rre):.2f} deg (max {np.max(rre):.2f}) | ARCore path {path_gt:.2f} m") + res[name] = (s, Rm, t) + +# point clouds in ARCore frame (metric), compare each other +def load_ply(p): + with open(p, "rb") as f: + hdr = b"" + while not hdr.endswith(b"end_header\n"): + hdr += f.readline() + h = hdr.decode() + n = int([l for l in h.splitlines() if l.startswith("element vertex")][0].split()[-1]) + props = [l.split() for l in h.splitlines() if l.startswith("property")] + dt = np.dtype([(p[2], {"float": " +Loading retrieval model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(modelname, 'cpu') # TODO from pretrained to download it automatically +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/mast3r_slam/frame.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1729647429097/work/torch/csrc/utils/tensor_numpy.cpp:206.) + uimg = torch.from_numpy(img["unnormalized_img"]) / 255.0 +FPS: 12.33590281636621 +FPS: 11.607569169553011 +FPS: 12.396332322940951 +Database retrieval 4 : {1, 2} +Database retrieval 5 : {0, 1} +FPS: 12.492613825691176 +Database retrieval 6 : {0} +Database retrieval 7 : {0, 5} +FPS: 12.57538713984326 +Database retrieval 8 : {3, 4, 5} +FPS: 12.725914586903064 +Database retrieval 9 : {3, 4} +Database retrieval 10 : {8, 3} +FPS: 12.756718092517463 +Database retrieval 11 : {8, 7} +Database retrieval 12 : {6, 7} +FPS: 12.759113119333195 +Database retrieval 13 : {0, 8} +Database retrieval 14 : {1, 4} +FPS: 12.762811197903137 +FPS: 12.880627745361014 +FPS: 12.976594873552434 +FPS: 13.044997218169186 +FPS: 13.072639419033358 +FPS: 13.140286243507285 +Database retrieval 21 : {19} +FPS: 13.12991393048011 +FPS: 13.196382112552827 +FPS: 13.216374297188427 +Database retrieval 27 : {24, 25} +FPS: 13.199489486094324 +FPS: 13.237172780527683 +Database retrieval 32 : {11} +FPS: 13.223430163172521 +Database retrieval 33 : {10, 11} +FPS: 13.251694478320214 +Database retrieval 34 : {32, 11} +Skipped frame 642 +RELOCALIZING against kf 35 and [31] +Success! Relocalized +Database retrieval 36 : {30} +FPS: 13.227187879597565 +Database retrieval 37 : {29, 30} +Database retrieval 38 : {29} +Database retrieval 39 : {29, 37} +Database retrieval 40 : {37, 38} +Database retrieval 41 : {39} +FPS: 13.077595652070865 +FPS: 13.129983085102474 +Database retrieval 43 : {28, 29} +FPS: 13.11678793475404 +Database retrieval 44 : {27, 28} +FPS: 13.127350430316985 +Database retrieval 45 : {43, 28} +FPS: 13.135421645978152 +Database retrieval 46 : {27, 44} +Database retrieval 47 : {44} +Database retrieval 50 : {48, 21} +FPS: 13.029650409775764 +Database retrieval 51 : {48, 49} +Database retrieval 52 : {48, 50} +Database retrieval 53 : {51, 21} +Database retrieval 54 : {24, 47} +FPS: 12.928280263389755 +Database retrieval 55 : {53, 47} +Database retrieval 56 : {47, 46} +FPS: 12.906014238542092 +Database retrieval 57 : {45, 46} +FPS: 12.902951352817785 +Database retrieval 60 : {48, 25, 53} +Database retrieval 61 : {25, 27, 47} +FPS: 12.858207878128619 +Database retrieval 62 : {44, 46} +FPS: 12.850043096046894 +FPS: 12.844473611733065 +Database retrieval 66 : {64} +FPS: 12.859981476684911 +Database retrieval 67 : {64, 65} +Database retrieval 68 : {65} +FPS: 12.838983201810363 +Database retrieval 69 : {65, 67} +FPS: 12.851881774073115 +FPS: 12.85188193465458 +FPS: 12.858740692226574 +Database retrieval 73 : {64} +FPS: 12.84177846444868 +Database retrieval 75 : {72, 73, 41} +FPS: 12.837637371373422 +Database retrieval 76 : {27, 44} +Database retrieval 77 : {27} +Database retrieval 78 : {76} +Database retrieval 79 : {76, 77} +FPS: 12.779720455715667 +Database retrieval 80 : {77, 78} +Database retrieval 81 : {10, 79} +Database retrieval 82 : {8, 11} +FPS: 12.742462296601914 +Database retrieval 83 : {81, 78, 79} +FPS: 12.74943726812595 +FPS: 12.784943657209247 +Database retrieval 84 : {77, 78} +FPS: 12.79499635212129 +Database retrieval 85 : {83, 77} +FPS: 12.802284871441994 +Database retrieval 86 : {84} +FPS: 12.79312375887982 +Database retrieval 89 : {84, 85} +FPS: 12.765872114404441 +Database retrieval 92 : {83, 7} +FPS: 12.756262385778216 +Database retrieval 93 : {91} +Database retrieval 94 : {91, 92} +Database retrieval 95 : {92, 93} +Database retrieval 96 : {93, 94} +Database retrieval 97 : {94, 95} +Database retrieval 98 : {96, 95} +Database retrieval 99 : {96, 97} +Database retrieval 100 : {97, 98} +Database retrieval 101 : {96, 98, 99} +Database retrieval 102 : {98, 95} +FPS: 12.545392936124507 +Database retrieval 103 : {94, 95} +Database retrieval 104 : {93, 94} +FPS: 12.534000425516425 +FPS: 12.553901307900604 +FPS: 12.542895957702026 +FPS: 12.538760675493679 +FPS: 12.551287059907454 +Database retrieval 114 : {87} +FPS: 12.533937420667218 +Database retrieval 115 : {88, 87} +Database retrieval 116 : {114, 87} +Database retrieval 117 : {89, 86} +Database retrieval 118 : {88, 111} +FPS: 12.473045138872017 +FPS: 12.471240684983666 +FPS: 12.484531044732917 +FPS: 12.500522368598782 +FPS: 12.513712662548265 +FPS: 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0.0659971758723259 0.483423113822937 0.4205864667892456 +61.599998474121094 -0.2461913824081421 1.6886587142944336 0.3940103352069855 0.8145961761474609 0.05730623006820679 0.47030380368232727 0.33460932970046997 +62.79999923706055 -0.24977277219295502 0.9925729632377625 0.38117554783821106 0.8440840840339661 0.011660137213766575 0.5300451517105103 0.08024050295352936 diff --git a/I12X/MasterSlam1_ScanVideo1/t3_frames.py b/I12X/MasterSlam1_ScanVideo1/t3_frames.py new file mode 100644 index 0000000000000000000000000000000000000000..3bc94fd578e5beb40cdfa627cd5589246dce5f1e --- /dev/null +++ b/I12X/MasterSlam1_ScanVideo1/t3_frames.py @@ -0,0 +1,45 @@ +"""T3 capture -> PNG frame folder for MASt3R-SLAM (RGBFiles only globs *.png) + calib yaml. + +NV21 .yuv frames are converted; JPEG frames are symlinked as NNNNNN.png (cv2.imread reads by content). + python t3_frames.py +""" +import json +import os +import sys +from multiprocessing import Pool +from pathlib import Path + +import cv2 +import numpy as np + +src, out = Path(sys.argv[1]), Path(sys.argv[2]) +intr = json.load(open(src / "intrinsics.json"))["image"] +W, H = intr["width"], intr["height"] +png_dir = out / "scan" +png_dir.mkdir(parents=True, exist_ok=True) + + +def conv(f): + dst = png_dir / (f.stem + ".png") + if dst.exists() or dst.is_symlink(): + return + if f.suffix == ".yuv": + yuv = np.fromfile(f, np.uint8) + assert yuv.size == W * H * 3 // 2, (f, yuv.size) + cv2.imwrite(str(dst), cv2.cvtColor(yuv.reshape(H * 3 // 2, W), cv2.COLOR_YUV2BGR_NV21)) + else: + os.symlink(f.resolve(), dst) + + +if __name__ == "__main__": + files = sorted(f for f in (src / "rgb").iterdir() if f.suffix in (".yuv", ".jpg", ".jpeg")) + with Pool(16) as p: + p.map(conv, files, chunksize=8) + img = cv2.imread(str(png_dir / (files[0].stem + ".png"))) + assert img is not None and img.shape[:2] == (H, W), (img is None or img.shape) + print(f"{len(files)} frames ({files[0].suffix}) -> {png_dir}, {W}x{H}") + fx, fy, cx, cy = intr["fx"], intr["fy"], intr["cx"], intr["cy"] + calib = (f"# From {src}/intrinsics.json (ARCore, sensor-oriented {W}x{H} image)\n" + f"width: {W}\nheight: {H}\ncalibration: [{fx}, {fy}, {cx}, {cy}]\n") + (out / "calib_arcore.yaml").write_text(calib) + print(calib) diff --git a/I12X/MasterSlam2_ScanVideo1/calib_arcore.yaml b/I12X/MasterSlam2_ScanVideo1/calib_arcore.yaml new file mode 100644 index 0000000000000000000000000000000000000000..48fb0caf64987d86eb77560f56755cd130a3d905 --- /dev/null +++ b/I12X/MasterSlam2_ScanVideo1/calib_arcore.yaml @@ -0,0 +1,4 @@ +# From /home/lehoangan/LHAN/RoSE/I12X/ScanVideos/Video1_T3/intrinsics.json (ARCore, sensor-oriented 1920x1080 image) +width: 1920 +height: 1080 +calibration: [1293.4334716796875, 1296.693603515625, 1001.7452392578125, 545.7500610351562] diff --git a/I12X/MasterSlam2_ScanVideo1/config.yaml b/I12X/MasterSlam2_ScanVideo1/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa858aeba51d65509d2d3f807db445b5396ef31d --- /dev/null +++ b/I12X/MasterSlam2_ScanVideo1/config.yaml @@ -0,0 +1,58 @@ +use_calib: False +single_thread: False +dataset: + subsample: 1 + img_downsample: 1 + center_principle_point: True + +matching: + max_iter: 10 + lambda_init: 1e-8 + convergence_thresh: 1e-6 + dist_thresh: 1e-1 # distance in 3D space + radius: 3 + dilation_max: 5 # Right now starts from max and iteratively decreases until 1 + +tracking: + min_match_frac: 0.05 + max_iters: 50 + C_conf: 0.0 + Q_conf: 1.5 + rel_error: 1e-3 + delta_norm: 1e-3 + huber: 1.345 + match_frac_thresh: 0.333 + sigma_ray: 0.003 + sigma_dist: 1e+1 + sigma_pixel: 1.0 + sigma_depth: 1e+1 # NOTE: log-depth! + sigma_point: 0.05 + pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance) + depth_eps: 1e-6 # Only in calib case + filtering_mode: weighted_pointmap # recent, first, best_score, weighted_pointmap, weighted_spherical, indep_conf + filtering_score: median # median, mean (only used for filtering_mode=best_score) + +local_opt: + pin: 1 + window_size: 1e+6 + C_conf: 0.0 + Q_conf: 1.5 + min_match_frac: 0.1 + pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance) + depth_eps: 1e-6 # Only in calib case + max_iters: 10 + sigma_ray: 0.003 + sigma_dist: 1e+1 + sigma_pixel: 1.0 + sigma_depth: 1e+1 # NOTE: log-depth! + sigma_point: 0.05 + delta_norm: 1e-8 + use_cuda: True + +retrieval: + k: 3 + min_thresh: 5e-3 + +reloc: + min_match_frac: 0.3 + strict: True \ No newline at end of file diff --git a/I12X/MasterSlam2_ScanVideo1/keyframes/scan/0.0.png b/I12X/MasterSlam2_ScanVideo1/keyframes/scan/0.0.png new file mode 100644 index 0000000000000000000000000000000000000000..6e976eddcf715b66d3ec94b5c6e93bf200f0bbcb --- /dev/null +++ b/I12X/MasterSlam2_ScanVideo1/keyframes/scan/0.0.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee95ac53aaa124d5c000407834c62f18b9bcb416ba6c58f2f9a1103100f15c6b +size 284395 diff --git 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1e-06, 'dist_thresh': 0.1, 'radius': 3, 'dilation_max': 5}, 'tracking': {'min_match_frac': 0.05, 'max_iters': 50, 'C_conf': 0.0, 'Q_conf': 1.5, 'rel_error': 0.001, 'delta_norm': 0.001, 'huber': 1.345, 'match_frac_thresh': 0.333, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'pixel_border': -10, 'depth_eps': 1e-06, 'filtering_mode': 'weighted_pointmap', 'filtering_score': 'median'}, 'local_opt': {'pin': 1, 'window_size': 1000000.0, 'C_conf': 0.0, 'Q_conf': 1.5, 'min_match_frac': 0.1, 'pixel_border': -10, 'depth_eps': 1e-06, 'max_iters': 10, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'delta_norm': 1e-08, 'use_cuda': True}, 'retrieval': {'k': 3, 'min_thresh': 0.005}, 'reloc': {'min_match_frac': 0.3, 'strict': True}} +... loading model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(model_path, map_location='cpu') +instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False) + +Loading retrieval model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(modelname, 'cpu') # TODO from pretrained to download it automatically +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/mast3r_slam/frame.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1729647429097/work/torch/csrc/utils/tensor_numpy.cpp:206.) + uimg = torch.from_numpy(img["unnormalized_img"]) / 255.0 +FPS: 11.933352193403028 +FPS: 12.612756303966446 +Database retrieval 3 : {1} +FPS: 12.94265659133114 +Database retrieval 4 : {1, 2} +Database retrieval 5 : {0, 1} +FPS: 12.777238464356708 +Database retrieval 6 : {0} +Database retrieval 7 : {5} +FPS: 12.723774875846516 +Database retrieval 8 : {0, 5} +Database retrieval 9 : {3, 4} +FPS: 12.67192381686426 +Database retrieval 10 : {3, 4} +Database retrieval 11 : {8} +FPS: 12.647220405644042 +Database retrieval 12 : {8, 7} +Database retrieval 13 : {8, 6, 7} +FPS: 12.565413718990277 +Database retrieval 14 : {8, 5} +FPS: 12.628590312749566 +Database retrieval 15 : {2} +Database retrieval 16 : {14} +FPS: 12.642682557473316 +FPS: 12.721592625529421 +FPS: 12.816766943259495 +FPS: 12.77759747070556 +FPS: 12.831359472748801 +Database retrieval 22 : {20} +FPS: 12.796029183597934 +FPS: 12.844294271469638 +FPS: 12.814680958125322 +Database retrieval 29 : {26, 27} +Database retrieval 30 : {26} +FPS: 12.778933960528992 +FPS: 12.774036870732813 +FPS: 12.787566776199654 +Database retrieval 35 : {11} +FPS: 12.813107468612085 +Database retrieval 37 : {34} +Database retrieval 38 : {32, 33} +FPS: 12.768021616669232 +Database retrieval 39 : {32, 31} +Database retrieval 40 : {32, 38} +Database retrieval 41 : {31, 39} +Database retrieval 42 : {40, 39} +FPS: 12.62831915302692 +FPS: 12.645453515169597 +Database retrieval 44 : {31} +Database retrieval 45 : {30, 31} +FPS: 12.60998639739439 +FPS: 12.656167014262557 +Database retrieval 46 : {44} +FPS: 12.664506837748666 +Database retrieval 47 : {45, 30} +Database retrieval 51 : {49} +Database retrieval 52 : {49, 50} +FPS: 12.518011920128242 +Database retrieval 53 : {50, 51} +Database retrieval 54 : {52, 22} +Database retrieval 55 : {53, 22} +Database retrieval 56 : {48, 54} +FPS: 12.41034841616568 +Database retrieval 57 : {48, 55} +Database retrieval 58 : {48, 47} +FPS: 12.378508860339815 +FPS: 12.404342802320633 +Database retrieval 62 : {49, 26, 55} +FPS: 12.351272643259696 +Database retrieval 63 : {48, 29} +FPS: 12.313316232102263 +FPS: 12.092145585180253 +FPS: 12.133331307079512 +FPS: 12.153550849201459 +Database retrieval 68 : {66} +Database retrieval 69 : {67} +FPS: 12.146508943825966 +FPS: 12.16555855537998 +FPS: 12.159288409115302 +Database retrieval 74 : {71} +FPS: 12.152074096648 +Database retrieval 75 : {66, 71} +FPS: 12.160359698536714 +Database retrieval 76 : {29, 71} +Database retrieval 77 : {71} +Database retrieval 78 : {76} +Database retrieval 79 : {77} +FPS: 12.103311287598796 +Database retrieval 80 : {78} +Database retrieval 81 : {79} +Database retrieval 82 : {11, 12} +FPS: 12.073561650307107 +Database retrieval 83 : {80, 81, 79} +FPS: 12.081633572964083 +FPS: 12.119708432495488 +Database retrieval 84 : {77, 78} +FPS: 12.131007675693317 +Database retrieval 85 : {78, 76} +FPS: 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b/I12X/MasterSlam3_ScanVideo2/config.yaml @@ -0,0 +1,58 @@ +use_calib: False +single_thread: False +dataset: + subsample: 1 + img_downsample: 1 + center_principle_point: True + +matching: + max_iter: 10 + lambda_init: 1e-8 + convergence_thresh: 1e-6 + dist_thresh: 1e-1 # distance in 3D space + radius: 3 + dilation_max: 5 # Right now starts from max and iteratively decreases until 1 + +tracking: + min_match_frac: 0.05 + max_iters: 50 + C_conf: 0.0 + Q_conf: 1.5 + rel_error: 1e-3 + delta_norm: 1e-3 + huber: 1.345 + match_frac_thresh: 0.333 + sigma_ray: 0.003 + sigma_dist: 1e+1 + sigma_pixel: 1.0 + sigma_depth: 1e+1 # NOTE: log-depth! + sigma_point: 0.05 + pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance) + depth_eps: 1e-6 # Only in calib case + filtering_mode: weighted_pointmap # recent, first, best_score, weighted_pointmap, weighted_spherical, indep_conf + filtering_score: median # median, mean (only used for filtering_mode=best_score) + 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'tracking': {'min_match_frac': 0.05, 'max_iters': 50, 'C_conf': 0.0, 'Q_conf': 1.5, 'rel_error': 0.001, 'delta_norm': 0.001, 'huber': 1.345, 'match_frac_thresh': 0.333, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'pixel_border': -10, 'depth_eps': 1e-06, 'filtering_mode': 'weighted_pointmap', 'filtering_score': 'median'}, 'local_opt': {'pin': 1, 'window_size': 1000000.0, 'C_conf': 0.0, 'Q_conf': 1.5, 'min_match_frac': 0.1, 'pixel_border': -10, 'depth_eps': 1e-06, 'max_iters': 10, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'delta_norm': 1e-08, 'use_cuda': True}, 'retrieval': {'k': 3, 'min_thresh': 0.005}, 'reloc': {'min_match_frac': 0.3, 'strict': True}} +... loading model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(model_path, map_location='cpu') +instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False) + +Loading retrieval model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(modelname, 'cpu') # TODO from pretrained to download it automatically +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/mast3r_slam/frame.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1729647429097/work/torch/csrc/utils/tensor_numpy.cpp:206.) + uimg = torch.from_numpy(img["unnormalized_img"]) / 255.0 +FPS: 7.057641303290668 +FPS: 8.159269959728602 +FPS: 8.536060863812708 +Database retrieval 6 : {4} +FPS: 8.661138816593683 +Database retrieval 10 : {8} +Database retrieval 11 : {8, 9} +Database retrieval 12 : {9, 10} +FPS: 8.51687659022461 +Database retrieval 13 : {11} +Database retrieval 14 : {12} +FPS: 8.568023555658632 +FPS: 8.639612269074803 +Database retrieval 17 : {15} +FPS: 8.723453548108253 +Database retrieval 18 : {15} +Database retrieval 19 : {7, 15} +FPS: 8.726253172561574 +Database retrieval 20 : {18} +FPS: 8.76804741465419 +FPS: 8.826797398708742 +Database retrieval 22 : {20} +FPS: 8.85742636126161 +Database retrieval 25 : {23} +Database retrieval 26 : {24, 23} +FPS: 8.884331703234253 +FPS: 8.9151305589014 +FPS: 8.930908608165756 +FPS: 8.944254850990937 +Database retrieval 33 : {31} +Database retrieval 34 : {30} +FPS: 8.934923639537036 +Database retrieval 36 : {29} +FPS: 8.970755403771893 +Database retrieval 37 : {24, 26, 29} +FPS: 8.98895163013319 +Database retrieval 38 : {27} +Database retrieval 41 : {34} +FPS: 8.966143323146659 +FPS: 8.977988795081215 +Database retrieval 47 : {45} +Database retrieval 48 : {46} +FPS: 8.94141848654124 +FPS: 8.973628410006286 +FPS: 9.006128216387314 +Database retrieval 54 : {26, 3} +Database retrieval 55 : {3} +FPS: 8.99263427564088 +Database retrieval 56 : {4} +Database retrieval 58 : {56} +FPS: 8.98949898119966 +FPS: 9.014921902847936 +FPS: 9.025822817225182 +FPS: 9.047005443362362 +Database retrieval 64 : {3, 55} +FPS: 9.053323925619505 +Database retrieval 66 : {51} +FPS: 9.069837739095341 +Database retrieval 67 : {1, 51} +Database retrieval 68 : {48, 47} +Database retrieval 69 : {46, 47} +FPS: 9.054922386896896 +Database retrieval 70 : {45, 63} +Database retrieval 71 : {62} +Database retrieval 72 : {62} +FPS: 9.036363114036346 +FPS: 9.036819811573611 +FPS: 9.048636709551326 +Database retrieval 78 : {76} +Database retrieval 79 : {75, 76} +Database retrieval 80 : {75, 76} +FPS: 9.035924586233403 +Skipped frame 1085 +FPS: 9.046656601395497 +RELOCALIZING against kf 81 and [14] +Failed to relocalize +RELOCALIZING against kf 81 and [14] +Failed to relocalize +FPS: 9.044403633072655 +FPS: 9.051946925655571 +RELOCALIZING against kf 81 and [77] +Failed to relocalize +RELOCALIZING against kf 81 and [77] +Failed to relocalize +RELOCALIZING against kf 81 and [77] +Failed to relocalize +RELOCALIZING against kf 81 and [77] +Failed to relocalize +RELOCALIZING against kf 81 and [64] +Failed to relocalize +RELOCALIZING against kf 81 and [64] +Failed to relocalize +RELOCALIZING against kf 81 and [64] +Failed to relocalize +RELOCALIZING against kf 81 and [64] +Failed to relocalize +RELOCALIZING against kf 81 and [64] +Failed to relocalize +FPS: 9.04487523349094 +RELOCALIZING against kf 81 and [64] +Failed to relocalize +RELOCALIZING against kf 81 and [64] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14, 13] +Failed to relocalize +RELOCALIZING against kf 81 and [14] +Failed to relocalize +RELOCALIZING against kf 81 and [13, 14] +Failed to relocalize +RELOCALIZING against kf 81 and [13] +Failed to relocalize +RELOCALIZING against kf 81 and [13, 14] +Failed to relocalize +RELOCALIZING against kf 81 and [14] +Failed to relocalize +FPS: 8.97152759328119 +FPS: 8.97073349150474 +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1, 2] +Failed to relocalize +RELOCALIZING against kf 81 and [1, 2] +Failed to relocalize +RELOCALIZING against kf 81 and [1, 2] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [1] +Failed to relocalize +FPS: 8.931097409495184 +RELOCALIZING against kf 81 and [1] +Failed to relocalize +RELOCALIZING against kf 81 and [9] +Failed to relocalize +RELOCALIZING against kf 81 and [3, 9] +Failed to relocalize +RELOCALIZING against kf 81 and [3] +Success! Relocalized +Database retrieval 82 : {3, 54} +Database retrieval 83 : {81, 54} +Database retrieval 84 : {81, 82} +Database retrieval 85 : {3, 83} +FPS: 8.882253518167593 +Database retrieval 86 : {84, 55} +Database retrieval 87 : {5} +Database retrieval 88 : {51} +FPS: 8.874851697437933 +FPS: 8.868648428402906 +Database retrieval 92 : {90} +FPS: 8.8724396659164 +Database retrieval 93 : {91} +FPS: 8.858277242782087 +FPS: 8.86608243786593 +FPS: 8.865303139454845 +FPS: 8.875415171040558 +Database retrieval 103 : {101} +FPS: 8.887621549193456 +Database retrieval 104 : {101, 102} +Database retrieval 105 : {16} +FPS: 8.878589229635015 +Database retrieval 107 : {96, 7} +Database retrieval 108 : {95} +FPS: 8.874684970133771 +Database retrieval 109 : {105} +FPS: 8.87589799526323 +Database retrieval 111 : {98, 99} +Database retrieval 112 : {96, 107, 108} +FPS: 8.872184113698356 +Database retrieval 113 : {107, 96} +done diff --git a/I12X/MasterSlam3_ScanVideo2/scan.ply 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a/I12X/MasterSlam4_ScanVideo2/config.yaml b/I12X/MasterSlam4_ScanVideo2/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa858aeba51d65509d2d3f807db445b5396ef31d --- /dev/null +++ b/I12X/MasterSlam4_ScanVideo2/config.yaml @@ -0,0 +1,58 @@ +use_calib: False +single_thread: False +dataset: + subsample: 1 + img_downsample: 1 + center_principle_point: True + +matching: + max_iter: 10 + lambda_init: 1e-8 + convergence_thresh: 1e-6 + dist_thresh: 1e-1 # distance in 3D space + radius: 3 + dilation_max: 5 # Right now starts from max and iteratively decreases until 1 + +tracking: + min_match_frac: 0.05 + max_iters: 50 + C_conf: 0.0 + Q_conf: 1.5 + rel_error: 1e-3 + delta_norm: 1e-3 + huber: 1.345 + match_frac_thresh: 0.333 + sigma_ray: 0.003 + sigma_dist: 1e+1 + sigma_pixel: 1.0 + sigma_depth: 1e+1 # NOTE: log-depth! + sigma_point: 0.05 + pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance) + depth_eps: 1e-6 # Only in calib case + filtering_mode: weighted_pointmap # recent, first, best_score, weighted_pointmap, weighted_spherical, indep_conf + filtering_score: median # median, mean (only used for filtering_mode=best_score) + +local_opt: + pin: 1 + window_size: 1e+6 + C_conf: 0.0 + Q_conf: 1.5 + min_match_frac: 0.1 + pixel_border: -10 # Only in calib (negative means allow pixels outside image up to that distance) + depth_eps: 1e-6 # Only in calib case + max_iters: 10 + sigma_ray: 0.003 + sigma_dist: 1e+1 + sigma_pixel: 1.0 + sigma_depth: 1e+1 # NOTE: log-depth! + sigma_point: 0.05 + delta_norm: 1e-8 + use_cuda: True + +retrieval: + k: 3 + min_thresh: 5e-3 + +reloc: + min_match_frac: 0.3 + strict: True \ No newline at end of file diff --git a/I12X/MasterSlam4_ScanVideo2/keyframes/scan/0.0.png b/I12X/MasterSlam4_ScanVideo2/keyframes/scan/0.0.png new file mode 100644 index 0000000000000000000000000000000000000000..fd25988556dfb6fc909fe0b7a2339356cf07fbc1 --- /dev/null +++ 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'weighted_pointmap', 'filtering_score': 'median'}, 'local_opt': {'pin': 1, 'window_size': 1000000.0, 'C_conf': 0.0, 'Q_conf': 1.5, 'min_match_frac': 0.1, 'pixel_border': -10, 'depth_eps': 1e-06, 'max_iters': 10, 'sigma_ray': 0.003, 'sigma_dist': 10.0, 'sigma_pixel': 1.0, 'sigma_depth': 10.0, 'sigma_point': 0.05, 'delta_norm': 1e-08, 'use_cuda': True}, 'retrieval': {'k': 3, 'min_thresh': 0.005}, 'reloc': {'min_match_frac': 0.3, 'strict': True}} +... loading model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(model_path, map_location='cpu') +instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False) + +Loading retrieval model from checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/thirdparty/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + ckpt = torch.load(modelname, 'cpu') # TODO from pretrained to download it automatically +/home/lehoangan/LHAN/mast3r/MASt3R-SLAM/mast3r_slam/frame.py:116: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1729647429097/work/torch/csrc/utils/tensor_numpy.cpp:206.) + uimg = torch.from_numpy(img["unnormalized_img"]) / 255.0 +FPS: 8.723366612184819 +FPS: 9.073728326073267 +FPS: 9.129561816906033 +Database retrieval 6 : {4} +FPS: 9.083352942903675 +Database retrieval 10 : {8} +Database retrieval 11 : {8, 9} +FPS: 8.110761535252395 +Database retrieval 12 : {10} +Database retrieval 13 : {11} +FPS: 6.808059988537549 +FPS: 6.9184177421389705 +Database retrieval 17 : {15} +FPS: 7.125246361872491 +Database retrieval 18 : {14, 15} +Database retrieval 19 : {14} +FPS: 7.060264300632716 +Database retrieval 20 : {18} +FPS: 6.652456895394076 +FPS: 6.82618525280673 +Database retrieval 25 : {23} +FPS: 6.968556134247628 +Database retrieval 26 : {24, 23} +FPS: 7.107912831895332 +FPS: 7.220994448097693 +FPS: 7.312486752090762 +FPS: 7.396491752209989 +Database retrieval 33 : {31} +Database retrieval 34 : {30} +FPS: 7.455649249086186 +Database retrieval 36 : {29} +Database retrieval 37 : {26, 29, 23} +FPS: 7.521141241262042 +Database retrieval 38 : {25, 26} +FPS: 7.465825404801057 +Database retrieval 39 : {27, 37} +FPS: 7.503312492935407 +Database retrieval 43 : {33} +FPS: 7.559886627943778 +FPS: 7.5796186908681475 +FPS: 7.646299584704182 +FPS: 7.708043984858118 +Database retrieval 55 : {3} +FPS: 7.739017795050866 +Database retrieval 57 : {5} +Database retrieval 59 : {57} +FPS: 7.770228048470698 +Database retrieval 60 : {58} +FPS: 7.821196349348928 +FPS: 7.859222872937104 +FPS: 7.902279159960414 +Database retrieval 65 : {3} +Database retrieval 66 : {56} +FPS: 7.923765209132315 +Database retrieval 68 : {52} +Database retrieval 69 : {51, 52} +FPS: 7.944885948896375 +Database retrieval 71 : {48, 47} +FPS: 7.973825463955292 +Database retrieval 72 : {64, 46} +Database retrieval 73 : {63} +Database retrieval 74 : {63} +FPS: 7.9775296367801065 +FPS: 7.995534242121919 +FPS: 8.020952004901428 +Database retrieval 81 : {79} +Database retrieval 82 : {78, 79} +Database retrieval 83 : {78, 79} +FPS: 8.015533541226077 +Database retrieval 84 : {77, 78} +FPS: 8.01397436515527 +Database retrieval 88 : {13} +FPS: 8.02732068784013 +Database retrieval 92 : {89} +Database retrieval 93 : {91} +Database retrieval 94 : {92} +FPS: 8.015515369022781 +Database retrieval 97 : {66} +FPS: 7.841706491158787 +Database retrieval 98 : {96} +Database retrieval 99 : {88, 97} +Database retrieval 100 : {88, 13, 87} +FPS: 7.628338736610764 +Database retrieval 101 : {88, 87} +FPS: 7.4539241827875395 +FPS: 7.319200787178367 +Database retrieval 104 : {102} +Database retrieval 105 : {3, 55} +Database retrieval 106 : {104, 55} +Database retrieval 107 : {105, 55} +Database retrieval 108 : {106, 55} +Database retrieval 109 : {107, 55} +FPS: 7.183277435508375 +Database retrieval 110 : {4, 5} +FPS: 7.203271896328887 +FPS: 7.216811214842818 +Database retrieval 115 : {113} +FPS: 7.241179357128728 +Database retrieval 116 : {114} +FPS: 7.246855368380851 +FPS: 7.26994599904919 +Database retrieval 124 : {98} +FPS: 7.283260696861978 +Database retrieval 125 : {16} +FPS: 7.309549387497912 +Database retrieval 126 : {124} +FPS: 7.338160709043842 +Database retrieval 127 : {88, 125} +Database retrieval 128 : {16} +FPS: 7.345947068616893 +Database retrieval 130 : {7, 119} +Database retrieval 131 : {118} +FPS: 7.360589591723867 +Database retrieval 132 : {128, 100} +FPS: 7.3763028558712165 +Database retrieval 134 : {121, 122} +Database retrieval 135 : {120, 131, 119} +FPS: 7.388978672879405 +Database retrieval 136 : {130, 119} +done diff --git a/I12X/MasterSlam4_ScanVideo2/scan.ply b/I12X/MasterSlam4_ScanVideo2/scan.ply new file mode 100644 index 0000000000000000000000000000000000000000..638f3b43f956968f4eb51599378351e88ae2647b --- /dev/null +++ b/I12X/MasterSlam4_ScanVideo2/scan.ply @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:826f3b4f783f3032bc3e4bd5b8447fdd241c165f78ed7130e595a8a73dc14328 +size 248466287 diff --git 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Not every room has every folder. | Room | Contents | |----------|----------| | Bedroom1 | ScanVideos, EmbodiedVideos (4), MasterSlam1_ScanVideo1, Spatiallm1_MasterSlam1, NeuCon1 | -| I12X | ScanVideos (`Video1_T3`, `Video2_T3`), EmbodiedVideos (2), RTABMAP1, RTABMAP2 | +| I12X | ScanVideos (`Video1_T3`, `Video2_T3`), EmbodiedVideos (2), RTABMAP1, RTABMAP2, MasterSlam1_ScanVideo1, MasterSlam2_ScanVideo1, MasterSlam3_ScanVideo2, MasterSlam4_ScanVideo2 | | I64 | ScanVideos (`Video1_T3`), EmbodiedVideos (1), RTABMAP1, RTABMAP2, RTABMAP3 | | I87X | ScanVideos (`Video1.MOV`, `Video2_T3`), MasterSlam1_ScanVideo2, MasterSlam2_ScanVideo2, Spatiallm1_MasterSlam1, Spatiallm1_MasterSlam2 | | P63 | ScanVideos (`StrayScanner1`), MasterSlam1_StrayScanner1, Spatiallm1_MasterSlam1 | @@ -142,6 +142,20 @@ scene for Preview / Quick Look: each scan point is a 1.5 cm coloured tetrahedron with the ARCore intrinsics from `Video2_T3/intrinsics.json` (`--calib`). Input: the 1870 NV21 frames converted to PNG (1920x1080). Against the ARCore poses (Sim3-aligned), both have ~15 cm ATE; the calibrated run has about half the rotation error between keyframes (0.60 vs 1.16 deg). +- `I12X/MasterSlam1..4`: `base.yaml`. Runs 1 and 3 are uncalibrated; runs 2 and 4 use the ARCore intrinsics + (`--calib calib_arcore.yaml`). Runs 1-2 take `Video1_T3` (1915 JPEG frames), runs 3-4 take `Video2_T3` (1694 NV21 + frames converted to PNG); both are 1920x1080. Against the ARCore poses (Sim3-aligned, `compare_t3.py`): + + | Run | Input | Intrinsics | Keyframes | ATE rmse (max) | Rotation error between keyframes, mean (max) | Relocalizations | + |-----|-------|------------|-----------|----------------|-----------------------------------------------|-----------------| + | MasterSlam1_ScanVideo1 | Video1_T3 | no | 125 | 23.7 cm (52.5) | 1.35 deg (4.01) | 1 | + | MasterSlam2_ScanVideo1 | Video1_T3 | ARCore | 122 | 21.4 cm (42.1) | 0.80 deg (3.76) | 0 | + | MasterSlam3_ScanVideo2 | Video2_T3 | no | 114 | 47.9 cm (333.3) | 1.96 deg (25.78) | 48 | + | MasterSlam4_ScanVideo2 | Video2_T3 | ARCore | 137 | 14.1 cm (28.7) | 1.11 deg (5.14) | 0 | + + `MasterSlam3_ScanVideo2` lost tracking many times and has misplaced keyframes. Prefer `MasterSlam4_ScanVideo2`. + The scripts used for these runs (`t3_frames.py`: T3 frames to PNG plus calib file; `compare_t3.py`: comparison + against ARCore) are in `I12X/MasterSlam1_ScanVideo1`. - All `Spatiallm1_*` folders: fork `stage3/pipeline.py` (commit `a10e67a`; `.glb` re-exported with `7021237`, which closes wall gaps under 20 cm), Llama-1B, seeds 0 1 2, default 2.6 m ceiling height (sets the scale). Non-default options, recorded in `summary.json`: - `P64/Spatiallm1_MasterSlam1`: `--up_hint=-y`. With `--up_hint auto` the wrong axis was taken