Renumber SpatialLM/MetaScenes runs so each room has unique indices (Spatiallm1..6, Metascenes1..6)
5540305 verified | pretty_name: RoSE - Robotic Spatial Environment | |
| task_categories: | |
| - other | |
| tags: | |
| - slam | |
| - 3d-reconstruction | |
| - indoor-scenes | |
| - depth | |
| - arcore | |
| # RoSE: Robotic Spatial Environment | |
| Indoor room scans captured with phones/tablets, plus 3D reconstructions, SLAM | |
| point clouds and room layouts generated from them. Each top-level folder is one | |
| room. | |
| ## Layout | |
| ``` | |
| <RoomID>/ | |
| ScanVideos/ raw scan captures (input to every pipeline) | |
| EmbodiedVideos/ raw embodied videos (mp4) | |
| <Output>_<Input>/ processed data, see naming below | |
| ``` | |
| Room IDs (`Bedroom1`, `I12X`, `I64`, `I87X`, `P63`, `P64`) are room names. Treat them as | |
| unique IDs. Not every room has every folder. | |
| | Room | Contents | | |
| |----------|----------| | |
| | Bedroom1 | ScanVideos, EmbodiedVideos (4), MasterSlam1_ScanVideo1, Spatiallm1_MasterSlam1, NeuCon1, Metascenes1_Spatiallm1_MasterSlam1 | | |
| | I12X | ScanVideos (`Video1_T3`, `Video2_T3`), EmbodiedVideos (2), RTABMAP1, RTABMAP2, MasterSlam1_ScanVideo1, MasterSlam2_ScanVideo1, MasterSlam3_ScanVideo2, MasterSlam4_ScanVideo2, Spatiallm1_MasterSlam1, Spatiallm2_MasterSlam2, Spatiallm3_MasterSlam3, Spatiallm4_MasterSlam4, Metascenes1_Spatiallm1_MasterSlam1, Metascenes2_Spatiallm2_MasterSlam2, Metascenes3_Spatiallm3_MasterSlam3, Metascenes4_Spatiallm4_MasterSlam4, Spatiallm5_RTABMAP1, Spatiallm6_RTABMAP2, Metascenes5_Spatiallm5_RTABMAP1, Metascenes6_Spatiallm6_RTABMAP2 | | |
| | I64 | ScanVideos (`Video1_T3`), EmbodiedVideos (1), RTABMAP1, RTABMAP2, RTABMAP3, Spatiallm1_RTABMAP1, Metascenes1_Spatiallm1_RTABMAP1 | | |
| | I87X | ScanVideos (`Video1.MOV`, `Video2_T3`), MasterSlam1_ScanVideo2, MasterSlam2_ScanVideo2, Spatiallm1_MasterSlam1, Spatiallm2_MasterSlam2, Metascenes1_Spatiallm1_MasterSlam1, Metascenes2_Spatiallm2_MasterSlam2 | | |
| | P63 | ScanVideos (`StrayScanner1`), MasterSlam1_StrayScanner1, Spatiallm1_MasterSlam1, Metascenes1_Spatiallm1_MasterSlam1 | | |
| | P64 | ScanVideos (`Video1.MOV`), RTABMAP1, MasterSlam1_ScanVideo1, Spatiallm1_MasterSlam1, Metascenes1_Spatiallm1_MasterSlam1, Spatiallm2_RTABMAP1, Metascenes2_Spatiallm2_RTABMAP1 | | |
| ## Naming system | |
| Folders that are generated from other data are named **`<Output>_<Input>`**: | |
| the part before `_` is the tool that produced the folder (plus a run index), | |
| the part after is the data it took as input. Read it as "Output takes input | |
| from Input". | |
| - `MasterSlam1_ScanVideo1`: MASt3R-SLAM run 1, fed with scan video 1 | |
| (`ScanVideos/Video1.*` in the same room). | |
| - `MasterSlam2_ScanVideo2`: MASt3R-SLAM run 2, fed with scan capture 2 | |
| (`ScanVideos/Video2_T3` in I87X). | |
| - `MasterSlam1_StrayScanner1`: MASt3R-SLAM run 1, fed with the Stray Scanner | |
| capture `ScanVideos/StrayScanner1`. | |
| - `Spatiallm1_MasterSlam1`: SpatialLM run 1, fed with the output of | |
| `MasterSlam1_*` in the same room (`Spatiallm2_MasterSlam2`: fed with `MasterSlam2_*`). | |
| - `Spatiallm5_RTABMAP1`: SpatialLM run 5 of that room, fed with `RTABMAP1/cloud.ply` in the same room. | |
| The run number counts every SpatialLM run in the room, whatever its input, so a room never has two | |
| folders with the same `SpatiallmN` (the same holds for `MasterSlamN` and `MetascenesN`). Later runs get the next number. | |
| - `Metascenes2_Spatiallm2_MasterSlam2`: MetaScenes-style run 2, fed with the folder | |
| `Spatiallm2_MasterSlam2` in the same room. Here the input part is a full folder name, since | |
| one room can have several `Spatiallm*_*` folders. | |
| Raw captures have no input, so they use plain names: | |
| - `VideoN.<ext>`: video number N from that room, recorded with a phone/tablet | |
| camera app. | |
| - `VideoN_T3`: capture number N recorded with the **T3** app. Here `_T3` names | |
| the app that wrote the data folder. | |
| - `StrayScanner1`: capture 1 recorded with the Stray Scanner app. | |
| - `NeuCon1`, `RTABMAP1`: tool output, run 1. | |
| A trailing number is a run/capture index within the room. | |
| ## Tools | |
| ### Capture | |
| | Tool | What | Output | | |
| |------|------|--------| | |
| | T3 | Custom-made ARCore Android app that records raw ARCore data. Run on a Samsung SM-S928B (Galaxy S24 Ultra). | `Video*_T3` | | |
| | Stray Scanner | iOS app, RGB + LiDAR depth + odometry. | `StrayScanner1` | | |
| | Phone/tablet camera apps | Plain video. Devices include Galaxy S24 Ultra, iPad Pro (11-inch, 2nd gen; P64), iPhone 14 Pro Max and a Samsung A-series phone. Per-file device is **TBD**, see Notes. | `ScanVideos/Video*.mp4/.MOV`, `EmbodiedVideos/*.mp4` | | |
| ### Processing / generation | |
| | Tool | Used for | Output | | |
| |------|----------|--------| | |
| | [MASt3R-SLAM](https://github.com/rmurai0610/MASt3R-SLAM) | Dense SLAM point cloud and camera trajectory from video (Colab or a GPU server; uncalibrated unless noted in Run notes) | `MasterSlam*_*` | | |
| | `room_plane_fit.py` (own script, in the folder) | Cleans the point cloud, aligns to gravity, fits wall planes, makes mesh, colliders and walls (Unity-ready) | `MasterSlam1_ScanVideo1/room_plane_fit_out/` | | |
| | `ply2obj.py` (own script) | Converts `scan.ply` to a textured mesh (`--mesh`: Poisson + baked texture) | `scan_mesh.obj/.mtl/.png` | | |
| | [SpatialLM](https://github.com/manycore-research/SpatialLM), run with the fork [lehoangan02/SpatialLM](https://github.com/lehoangan02/SpatialLM) (`stage3/pipeline.py`, commit `a10e67a`; `.glb` files re-exported with `7021237`) | Room layout (walls, objects) from a point cloud | `Spatiallm1_*/room.glb`, `room_with_scan.glb` | | |
| | MetaScenes-style asset replacement (own scripts in `P64/Metascenes1_Spatiallm1_MasterSlam1/`), adapted from [MetaScenes](https://github.com/meta-scenes/MetaScenes) | Replaces each SpatialLM box with an Objaverse asset, then runs MetaScenes' physics step | `Metascenes1_*/` | | |
| | NeuCon | Mesh reconstruction (trimesh export) | `Bedroom1/NeuCon1/neucon.ply` | | |
| | RTAB-Map (`.db` recorded on the phone/tablet app, exported with `rtabmap-export`) | RGB-D mapping, textured mesh and RGB point cloud | `RTABMAP*/` | | |
| ## File reference | |
| ### `VideoN_T3/` (T3 app, raw ARCore) | |
| | File | Content | | |
| |------|---------| | |
| | `rgb/` | RGB frames (JPEG; raw NV21 `.yuv` in `I12X/.../Video2_T3` and `I87X/.../Video2_T3`) | | |
| | `session.mp4` | Video of the session | | |
| | `depth/`, `depth_raw/`, `conf/` | ARCore depth, raw depth and confidence frames | | |
| | `points/` | ARCore feature points per frame (`.bin`) | | |
| | `cubemap/` | ARCore environmental-light cubemaps (`.bin`) | | |
| | `poses.jsonl` | Per-frame camera pose (`p` position, `q` quaternion), tracking state | | |
| | `planes.jsonl` | Detected planes | | |
| | `light.jsonl` | Light estimation | | |
| | `imu.csv` | IMU samples | | |
| | `intrinsics.json`, `device_info.json`, `meta.json` | Camera intrinsics, device and sensors, session info (fps, frames, duration) | | |
| ### `StrayScanner1/` (Stray Scanner) | |
| `rgb.mp4`, `depth/` (PNG), `confidence/`, `odometry.csv`, `camera_matrix.csv`, `imu.csv`. | |
| ### `MasterSlam1_*/` (MASt3R-SLAM) | |
| | File | Content | | |
| |------|---------| | |
| | `scan.ply` | Reconstructed point cloud | | |
| | `scan.txt` | Trajectory, one line per keyframe: `timestamp x y z qx qy qz qw` | | |
| | `keyframes/` | Keyframe images | | |
| | `run.log` | Run config and log | | |
| | `config.yaml` | MASt3R-SLAM config used (runs made on the GPU server) | | |
| | `calib_arcore.yaml` | Camera intrinsics passed with `--calib` (calibrated runs only) | | |
| | `scan_mesh.*` | Textured mesh from `ply2obj.py scan.ply scan_mesh.obj --mesh`; `scan_mesh.log` is the tool output | | |
| `MasterSlam1_ScanVideo1` also holds the scripts and outputs described above | |
| (`room_plane_fit.py`, `ply2obj.py`, `requirements.txt`, `room_plane_fit_out/`, | |
| `scan_mesh.*`). | |
| ### `RTABMAP*/` (RTAB-Map) | |
| | File | Content | | |
| |------|---------| | |
| | `<name>.obj/.mtl/.jpg` | Textured mesh exported from the RTAB-Map app (`<name>` = recording timestamp `yymmdd-HHMMSS`) | | |
| | `cloud.ply` | xyz + RGB point cloud, assembled from the depth images and optimized poses of the same `.db` | | |
| | `export_clouds.py` | Script that makes the `.ply` from `.db` files (kept in `I12X/RTABMAP1/`) | | |
| Producing the `.ply`: `python3 export_clouds.py <dir with .db files>` needs `rtabmap-export` on PATH and | |
| `open3d`. It runs `rtabmap-export --cloud` (writes `<db>_cloud.ply` with normals), then drops normals | |
| and curvature so viewers show the real colours, giving `<db>_rgb.ply`; that file is what is stored here, | |
| renamed to `cloud.ply` (the `<name>` of its `.db` is the same as the mesh in that folder). The `.db` files themselves are not in the dataset (they are large); a trailing | |
| `_1` on a db name is not part of `<name>`. Mapping: `P64/RTABMAP1` = `261104-120353`, `I64/RTABMAP1` = | |
| `261105-111119`, `I12X/RTABMAP1` = `261106-090437`, `I12X/RTABMAP2` = `261106-091133`. | |
| `I64/RTABMAP2` and `RTABMAP3` have no `.ply` yet (no `.db` available). | |
| ### `Spatiallm1_*/` | |
| `room.glb` is the predicted layout. `room_with_scan.glb` is the layout together | |
| with the input scan (300k scan points as a glTF point cloud; Apple Preview / Quick Look | |
| cannot show point clouds and draw them as spikes). `room_with_scan_preview.glb` is the same | |
| scene for Preview / Quick Look: each scan point is a 1.5 cm coloured tetrahedron (~34 MB). Each `Spatiallm*_*` folder also has | |
| `layout.txt` (SpatialLM text format, metres, z up, floor at z=0), `seeds.png` | |
| (top-down view of each seed), `summary.json` (settings, chosen seed, scores) and | |
| `pipeline.log`. | |
| ### `Metascenes1_*/` | |
| SpatialLM boxes replaced by Objaverse assets. Pipeline: | |
| 1. Crop the scan points inside each SpatialLM box. | |
| 2. Take 16 Objaverse-LVIS candidates of the matching category. | |
| 3. Rank them by ULIP-2 point-cloud embedding similarity to the crop (the backbone MetaScenes' Scan2Sim is | |
| trained from; the Scan2Sim checkpoint itself is gated) plus geometric fit. | |
| 4. Place the best one with MetaScenes' fixed-pose rule. | |
| 5. Run MetaScenes' MCMC overlap reduction and Blender rigid-body settling. | |
| | File | Content | | |
| |------|---------| | |
| | `scene_fixed.glb` | `room.glb` of the input folder + assets at their fixed pose (before physics) | | |
| | `scene_physics.glb` | Same after MCMC + physics | | |
| | `views.png` | Top and front views of both, over the scan | | |
| | `objects/*.glb` | Each placed asset, layout frame (z up, metres) | | |
| | `placements.json` | Per box: label, chosen Objaverse uid, scores, 4x4 transform | | |
| | `candidates.json` | All candidates per box with similarity, fit (chamfer, m), IoU, score | | |
| | `physics.json`, `physics.log` | Per-object movement from MCMC and simulation | | |
| | `assets_license.json` | Name, author, licence and Sketchfab link of each asset, for attribution | | |
| | `retrieval.log` | Retrieval output | | |
| ## Run notes | |
| - `P64/MasterSlam1_ScanVideo1`: MASt3R-SLAM `config/base.yaml`. Tracking was lost at frame 307 | |
| (~10 s of 64 s, fast blurred pan onto a white wall) and never recovered, so the cloud covers only | |
| part of the room (30 keyframes). A looser config tracked the whole video but gave a twisted room, | |
| so it was not kept. | |
| - `I87X/MasterSlam1_ScanVideo2`: `base.yaml`, uncalibrated. `I87X/MasterSlam2_ScanVideo2`: `base.yaml` | |
| 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 `Spatiallm*_*` 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 | |
| as up (0.085 raw height, scale x30). Only seed 0 found walls. | |
| - `I87X/Spatiallm2_MasterSlam2`: `--no_crop`. On the cropped cloud every seed looped on one | |
| zero-length wall and the export failed. | |
| - `I87X/Spatiallm1_MasterSlam1`: defaults. 11 of its 15 windows are exact repeats at one spot. | |
| - `P63/Spatiallm1_MasterSlam1`: defaults. 31 of its 43 boxes are `plants` (SpatialLM repeating). | |
| - `Bedroom1/Spatiallm1_MasterSlam1`: defaults. | |
| - `I12X/Spatiallm1_MasterSlam1` to `Spatiallm4_MasterSlam4`: defaults, run directly with commit `7021237`. Each covers the scanned | |
| part of the room as one rectangle (4 walls), except `Spatiallm3_MasterSlam3` (8 walls, 11 boxes), whose | |
| input `MasterSlam3_ScanVideo2` has misplaced keyframes, so its extra walls are not reliable. | |
| - `I12X/MasterSlam1..4`: `scan_mesh.*` made with `ply2obj.py scan.ply scan_mesh.obj --mesh` (default options). | |
| - `Metascenes*_*` (all rooms): ULIP-2 `PointBERT-10k-xyzrgb-vit_g` as ranker, 16 candidates, seed 0, | |
| 50 MCMC steps, 250 simulation frames, Blender 4.3 (`bpy`). Hanging objects (curtain, chandelier, | |
| painting) and carpets stay fixed. SpatialLM boxes often overlap (chairs inside table boxes), so the | |
| MCMC step can push objects away from where the scan has them; compare `scene_fixed.glb`. | |
| Asset licences (Objaverse annotation): 61 by, 3 by-sa. | |
| - `Bedroom1/Metascenes1_Spatiallm1_MasterSlam1`: 8 SpatialLM boxes, 8 after de-duplication (3 curtain, pillow, 2 computer, 2 chair); largest move in physics 1.03 m. | |
| - `P63/Metascenes1_Spatiallm1_MasterSlam1`: 43 SpatialLM boxes, 32 after de-duplication (painting, 5 curtain, 20 plants, sofa, chandelier, clothes_rack, 2 chair, carpet); largest move in physics 1.53 m. | |
| - `P64/Metascenes1_Spatiallm1_MasterSlam1`: 8 SpatialLM boxes, 8 after de-duplication (sideboard, 3 chair, side_table, coffee_table, chandelier, washing_cabinet); largest move in physics 0.64 m. | |
| - `I87X/Metascenes1_Spatiallm1_MasterSlam1`: 3 SpatialLM boxes, 3 after de-duplication (3 curtain); largest move in physics 0.00 m. | |
| - `I87X/Metascenes2_Spatiallm2_MasterSlam2`: 1 SpatialLM box, 1 after de-duplication (curtain); largest move in physics 0.00 m. | |
| - `I12X/Metascenes1_Spatiallm1_MasterSlam1`: 3 SpatialLM boxes, 3 after de-duplication (2 curtain, chandelier); largest move in physics 0.00 m. | |
| - `I12X/Metascenes2_Spatiallm2_MasterSlam2`: 1 SpatialLM box, 1 after de-duplication (curtain); largest move in physics 0.00 m. | |
| - `I12X/Metascenes3_Spatiallm3_MasterSlam3`: 11 SpatialLM boxes, 11 after de-duplication (7 curtain, painting, 3 chandelier); largest move in physics 0.00 m. | |
| - `I12X/Metascenes4_Spatiallm4_MasterSlam4`: 3 SpatialLM boxes, 3 after de-duplication (3 chandelier); largest move in physics 0.00 m. | |
| ## Notes | |
| - Which device recorded which video is still being confirmed (TBD). | |
| - Meaning of "EmbodiedVideos" and the NeuCon input are not yet documented (TBD). | |
| - License and citation: TBD. | |