text stringlengths 33 159 |
|---|
wall_0=Wall(1.7439626285658274,-6.177951328538402,-0.0736857955869825,5.168962628565827,-6.177951328538402,-0.0736857955869825,3.5600000000000005,0.0) |
wall_1=Wall(1.7439626285658274,-6.177951328538402,-0.0736857955869825,1.7439626285658274,-4.727951328538402,-0.0736857955869825,3.5600000000000005,0.0) |
wall_2=Wall(5.168962628565827,-6.177951328538402,-0.0736857955869825,5.168962628565827,-2.0779513285384024,-0.0736857955869825,3.5600000000000005,0.0) |
wall_3=Wall(-1.8560373714341725,-4.727951328538402,-0.0736857955869825,1.7439626285658274,-4.727951328538402,-0.0736857955869825,3.5600000000000005,0.0) |
wall_4=Wall(-1.8560373714341725,-4.727951328538402,-0.0736857955869825,-1.8560373714341725,-1.5279513285384017,-0.0736857955869825,3.5600000000000005,0.0) |
wall_5=Wall(5.168962628565827,-2.0779513285384024,-0.0736857955869825,6.343962628565828,-2.0779513285384024,-0.0736857955869825,3.5600000000000005,0.0) |
wall_6=Wall(6.343962628565828,-2.0779513285384024,-0.0736857955869825,6.343962628565828,-0.7779513285384017,-0.0736857955869825,3.5600000000000005,0.0) |
wall_7=Wall(-1.8560373714341725,-1.5279513285384017,-0.0736857955869825,-0.7060373714341723,-1.5279513285384017,-0.0736857955869825,3.5600000000000005,0.0) |
wall_8=Wall(-0.7060373714341723,-1.5279513285384017,-0.0736857955869825,-0.7060373714341723,3.7220486714615983,-0.0736857955869825,3.5600000000000005,0.0) |
wall_9=Wall(5.043962628565827,-0.7779513285384017,-0.0736857955869825,6.343962628565828,-0.7779513285384017,-0.0736857955869825,3.5600000000000005,0.0) |
wall_10=Wall(5.043962628565827,-0.7779513285384017,-0.0736857955869825,5.043962628565827,1.1720486714615976,-0.0736857955869825,3.5600000000000005,0.0) |
wall_11=Wall(1.4189626285658277,1.1720486714615976,-0.0736857955869825,5.043962628565827,1.1720486714615976,-0.0736857955869825,3.5600000000000005,0.0) |
wall_12=Wall(1.4189626285658277,1.1720486714615976,-0.0736857955869825,1.4189626285658277,3.7220486714615983,-0.0736857955869825,3.5600000000000005,0.0) |
wall_13=Wall(-0.7060373714341723,3.7220486714615983,-0.0736857955869825,1.4189626285658277,3.7220486714615983,-0.0736857955869825,3.5600000000000005,0.0) |
door_0=Door(wall_0,3.393962628565828,-6.177951328538402,1.1263142044130174,2.12,2.4000000000000004) |
door_1=Door(wall_2,5.168962628565827,-4.677951328538402,1.1263142044130174,0.9400000000000001,2.4000000000000004) |
door_2=Door(wall_4,-1.8560373714341725,-3.102951328538402,1.1263142044130174,1.8800000000000001,2.4000000000000004) |
door_3=Door(wall_6,6.343962628565828,-1.427951328538402,1.1263142044130174,1.2000000000000002,2.4000000000000004) |
door_4=Door(wall_7,-1.3810373714341724,-1.5279513285384017,1.1263142044130174,0.9400000000000001,2.4000000000000004) |
door_5=Door(wall_11,2.443962628565828,1.1720486714615976,1.1263142044130174,0.9400000000000001,2.4000000000000004) |
door_6=Door(wall_13,0.29396262856582767,3.7220486714615983,1.1263142044130174,0.9400000000000001,2.4000000000000004) |
bbox_0=Bbox(plants,2.143962628565828,-4.927951328538402,0.9263142044130175,-3.1416,0.828125,0.890625,2.0) |
bbox_1=Bbox(tv,-0.9810373714341725,-2.677951328538402,1.3013142044130175,-1.5708000000000002,1.828125,0.109375,1.109375) |
bbox_2=Bbox(plants,4.393962628565828,-2.552951328538402,0.9013142044130176,-3.1416,1.078125,1.09375,1.9375) |
bbox_3=Bbox(side_table,2.7939626285658274,-1.9029513285384017,0.5263142044130176,-3.1416,0.71875,0.71875,1.203125) |
bbox_4=Bbox(plants,4.193962628565828,-1.3529513285384018,0.9013142044130176,-3.1416,1.078125,1.09375,1.9375) |
bbox_5=Bbox(plants,3.1689626285658274,-0.32795132853840236,0.9013142044130176,-3.1416,1.078125,1.09375,1.9375) |
bbox_6=Bbox(plants,2.9939626285658276,-0.27795132853840165,0.9013142044130176,-3.1416,1.078125,1.09375,1.9375) |
bbox_7=Bbox(sofa,0.7939626285658277,-0.10295132853840183,0.47631420441301753,-1.5708000000000002,2.75,3.0,1.078125) |
bbox_8=Bbox(plants,3.9189626285658274,0.47204867146159835,0.9013142044130176,-3.1416,1.078125,1.09375,1.9375) |
bbox_9=Bbox(plants,4.468962628565828,0.7220486714615983,0.9013142044130176,-3.1416,1.078125,1.09375,1.9375) |
wall_0=Wall(147,0,3,284,0,3,178,0) |
wall_1=Wall(147,0,3,147,58,3,178,0) |
wall_2=Wall(284,0,3,284,164,3,178,0) |
wall_3=Wall(3,58,3,147,58,3,178,0) |
wall_4=Wall(3,58,3,3,186,3,178,0) |
wall_5=Wall(284,164,3,331,164,3,178,0) |
wall_6=Wall(331,164,3,331,216,3,178,0) |
wall_7=Wall(3,186,3,49,186,3,178,0) |
wall_8=Wall(49,186,3,49,396,3,178,0) |
wall_9=Wall(279,216,3,331,216,3,178,0) |
wall_10=Wall(279,216,3,279,294,3,178,0) |
wall_11=Wall(134,294,3,279,294,3,178,0) |
wall_12=Wall(134,294,3,134,396,3,178,0) |
wall_13=Wall(49,396,3,134,396,3,178,0) |
door_0=Door(wall_0,213,0,51,106,120) |
door_1=Door(wall_2,284,60,51,47,120) |
door_2=Door(wall_4,3,123,51,94,120) |
door_3=Door(wall_6,331,190,51,60,120) |
door_4=Door(wall_7,22,186,51,47,120) |
door_5=Door(wall_11,175,294,51,47,120) |
door_6=Door(wall_13,89,396,51,47,120) |
bbox_0=Bbox(plants,163,50,43,320,53,57,128) |
bbox_1=Bbox(tv,38,140,58,480,117,7,71) |
bbox_2=Bbox(plants,253,145,42,320,69,70,124) |
bbox_3=Bbox(side_table,189,171,27,320,46,46,77) |
bbox_4=Bbox(plants,245,193,42,320,69,70,124) |
bbox_5=Bbox(plants,204,234,42,320,69,70,124) |
bbox_6=Bbox(plants,197,236,42,320,69,70,124) |
bbox_7=Bbox(sofa,109,243,25,480,176,192,69) |
bbox_8=Bbox(plants,234,266,42,320,69,70,124) |
bbox_9=Bbox(plants,256,276,42,320,69,70,124) |
wall_0=Wall(1.7634906571414566,-6.2392458618355064,0.0017252562476698374,5.438490657141457,-6.2392458618355064,0.0017252562476698374,3.4600000000000004,0.0) |
wall_1=Wall(1.7634906571414566,-6.2392458618355064,0.0017252562476698374,1.7634906571414566,-4.789245861835506,0.0017252562476698374,3.4600000000000004,0.0) |
wall_2=Wall(5.438490657141457,-6.2392458618355064,0.0017252562476698374,5.438490657141457,-2.014245861835507,0.0017252562476698374,3.4600000000000004,0.0) |
wall_3=Wall(1.7634906571414566,-4.789245861835506,0.0017252562476698374,2.3134906571414566,-4.539245861835506,0.0017252562476698374,3.4600000000000004,0.0) |
wall_4=Wall(2.3134906571414566,-4.539245861835506,0.0017252562476698374,2.638490657141457,-4.3642458618355064,0.0017252562476698374,3.4600000000000004,0.0) |
wall_5=Wall(2.638490657141457,-4.3642458618355064,0.0017252562476698374,2.963490657141457,-4.039245861835506,0.0017252562476698374,3.4600000000000004,0.0) |
wall_6=Wall(2.963490657141457,-4.039245861835506,0.0017252562476698374,3.213490657141457,-3.7892458618355063,0.0017252562476698374,3.4600000000000004,0.0) |
wall_7=Wall(3.213490657141457,-3.7892458618355063,0.0017252562476698374,3.4884906571414573,-3.5892458618355065,0.0017252562476698374,3.4600000000000004,0.0) |
wall_8=Wall(3.4884906571414573,-3.5892458618355065,0.0017252562476698374,3.788490657141457,-3.2642458618355064,0.0017252562476698374,3.4600000000000004,0.0) |
wall_9=Wall(3.788490657141457,-3.2642458618355064,0.0017252562476698374,4.063490657141457,-3.1142458618355064,0.0017252562476698374,3.4600000000000004,0.0) |
wall_10=Wall(4.063490657141457,-3.1142458618355064,0.0017252562476698374,4.313490657141457,-2.9392458618355066,0.0017252562476698374,3.4600000000000004,0.0) |
wall_11=Wall(4.313490657141457,-2.9392458618355066,0.0017252562476698374,4.563490657141457,-2.7642458618355064,0.0017252562476698374,3.4600000000000004,0.0) |
wall_12=Wall(4.563490657141457,-2.7642458618355064,0.0017252562476698374,4.838490657141457,-2.5142458618355064,0.0017252562476698374,3.4600000000000004,0.0) |
wall_13=Wall(4.838490657141457,-2.5142458618355064,0.0017252562476698374,4.838490657141457,-2.014245861835507,0.0017252562476698374,3.4600000000000004,0.0) |
wall_14=Wall(-1.9115093428585432,-2.014245861835507,0.0017252562476698374,4.838490657141457,-2.014245861835507,0.0017252562476698374,3.4600000000000004,0.0) |
wall_15=Wall(-1.9115093428585432,-2.014245861835507,0.0017252562476698374,-1.9115093428585432,-0.7642458618355068,0.0017252562476698374,3.4600000000000004,0.0) |
wall_16=Wall(4.838490657141457,-2.014245861835507,0.0017252562476698374,8.288490657141455,-2.014245861835507,0.0017252562476698374,3.4600000000000004,0.0) |
wall_17=Wall(8.288490657141455,-2.014245861835507,0.0017252562476698374,8.288490657141455,-0.7642458618355068,0.0017252562476698374,3.4600000000000004,0.0) |
wall_18=Wall(-1.9115093428585432,-0.7642458618355068,0.0017252562476698374,8.288490657141455,-0.7642458618355068,0.0017252562476698374,3.4600000000000004,0.0) |
door_0=Door(wall_1,1.7634906571414566,-6.2392458618355064,1.5017252562476697,1.08,2.9800000000000004) |
door_1=Door(wall_2,5.438490657141457,-5.9892458618355064,1.5017252562476697,1.08,2.9800000000000004) |
door_2=Door(wall_15,-1.9115093428585432,-1.3642458618355064,1.5017252562476697,1.08,2.9800000000000004) |
door_3=Door(wall_18,-1.136509342858543,-0.7642458618355068,1.5017252562476697,1.08,2.9800000000000004) |
door_4=Door(wall_18,7.413490657141456,-0.7642458618355068,1.5017252562476697,1.08,2.9800000000000004) |
bbox_0=Bbox(plants,0.8634906571414567,-5.3642458618355064,0.9767252562476698,-1.5708000000000002,1.703125,1.625,1.921875) |
bbox_1=Bbox(plants,1.5634906571414569,-3.6392458618355064,1.5017252562476697,-1.5708000000000002,1.75,1.84375,3.0) |
bbox_2=Bbox(plants,3.8134906571414566,-2.5142458618355064,0.9517252562476699,-0.009817500000000479,1.4375,1.203125,1.890625) |
bbox_3=Bbox(sofa,1.3884906571414566,-1.4392458618355066,0.5267252562476699,-3.1416,1.859375,0.859375,1.046875) |
bbox_4=Bbox(plants,4.213490657141457,-1.214245861835506,0.9517252562476699,-0.009817500000000479,1.4375,1.203125,1.890625) |
wall_0=Wall(147,0,6,294,0,6,173,0) |
wall_1=Wall(147,0,6,147,58,6,173,0) |
wall_2=Wall(294,0,6,294,169,6,173,0) |
wall_3=Wall(147,58,6,169,68,6,173,0) |
wall_4=Wall(169,68,6,182,75,6,173,0) |
wall_5=Wall(182,75,6,195,88,6,173,0) |
wall_6=Wall(195,88,6,205,98,6,173,0) |
wall_7=Wall(205,98,6,216,106,6,173,0) |
wall_8=Wall(216,106,6,228,119,6,173,0) |
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Check out the documentation for more information.
Dreame 06-03 SpatialLM voxel comparison
This dataset contains Rerun recordings and SpatialLM text outputs for the same
colored Open3D TSDF point cloud reconstructed from the Dreame 06-03 active
stereo sequence. It compares SpatialLM point-cloud cleanup voxel sizes of
0.075 m and 0.025 m.
Files
| Path | Description |
|---|---|
active_stereo_tsdf_point_cloud.rrd |
Existing historical 0.075 m Rerun recording. |
runs/voxel_0.075/ |
Historical layout, raw generation, and diagnostics. |
runs/voxel_0.025/ |
New layout, raw generation, diagnostics, and standalone Rerun recording. |
comparison/active_stereo_tsdf_voxel_0.075_vs_0.025.rrd |
Three-view comparison: 0.075, 0.025, and overlay. |
comparison/comparison_report.json |
Input hashes, parameters, entity counts, runtime summaries, and coordinate audit. |
Open the comparison with Rerun 0.21 or a compatible viewer:
rerun comparison/active_stereo_tsdf_voxel_0.075_vs_0.025.rrd
The input point cloud is logged once in its original colors. The 0.075
layout is blue, the 0.025 layout is orange, and both can be toggled
independently. The recording explicitly uses a right-handed Z-up coordinate
frame and an XY grid.
Input provenance
- Source artifact:
active_stereo_tsdf_point_cloud.ply - SHA-256:
cfcfd27fae0174b4b69616a18c7c50e8fd4f19a4d8f0679624c15c51c207dc3e - Points: 175,025, with RGB colors and normals
- Unit: meters
- Coordinate transform applied before inference: identity
- Surface-normal Manhattan yaw audit:
-0.4434 degrees; no additional rotation was applied
The source PLY and model weights are not distributed in this dataset.
Inference settings
Both results use SpatialLM1.1-Qwen-0.5B, detect_type=all, seed 42, MPS
for the model and Sonata backbone, mtlgemm sparse convolution, SDPA attention,
FP16 LLM weights, block synchronization, 16-block convolution chunking, and a
maximum of 4096 generated tokens. The SpatialLM model grid is 0.025 m in
both runs.
| Metric | 0.075 m cleanup |
0.025 m cleanup |
|---|---|---|
| SpatialLM Mac commit | 8913c44 |
ba67311 |
| Points after cleanup | 29,460 | 119,767 |
| Tokens after grid sampling | 29,166 | 101,697 |
| Generated tokens | 896 | 946 |
| Walls | 19 | 14 |
| Doors | 5 | 7 |
| O-BBoxes | 5 | 10 |
| Total parsed entities | 29 | 31 |
All five Sonata stages completed on MPS with finite outputs. Irregular sparse
topology operations, including neighbor-map construction, serialization, and
large batch-offset reductions, use explicit and recorded CPU helpers for
watchdog safety and MPS correctness. PYTORCH_ENABLE_MPS_FALLBACK was not
enabled.
Comparison limitation
This is a historical visual comparison, not a strict single-variable
ablation. The 0.075 m result is intentionally reused from the existing
recording, while the 0.025 m result uses the later Mac backend commit that
fixes large MPS point-batch offsets. Differences may therefore reflect both
the cleanup voxel size and the backend revision. No claim is made that either
layout is geometrically correct.
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