rabbitKabbit/RoSE-Robotic-Spatial-Environment

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..4, Metascenes1_Spatiallm1_MasterSlam1, Metascenes1_Spatiallm1_MasterSlam2, Metascenes1_Spatiallm1_MasterSlam3, Metascenes1_Spatiallm1_MasterSlam4
I64	ScanVideos (Video1_T3), EmbodiedVideos (1), RTABMAP1, RTABMAP2, RTABMAP3
I87X	ScanVideos (Video1.MOV, Video2_T3), MasterSlam1_ScanVideo2, MasterSlam2_ScanVideo2, Spatiallm1_MasterSlam1, Spatiallm1_MasterSlam2, Metascenes1_Spatiallm1_MasterSlam1, Metascenes1_Spatiallm1_MasterSlam2
P63	ScanVideos (StrayScanner1), MasterSlam1_StrayScanner1, Spatiallm1_MasterSlam1, Metascenes1_Spatiallm1_MasterSlam1
P64	ScanVideos (Video1.MOV), RTABMAP1, MasterSlam1_ScanVideo1, Spatiallm1_MasterSlam1, Metascenes1_Spatiallm1_MasterSlam1

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 (Spatiallm1_MasterSlam2: fed with MasterSlam2_*).

• Metascenes1_Spatiallm1_MasterSlam2: MetaScenes-style run 1, fed with the folder Spatiallm1_MasterSlam2 in the same room. Here the input part is a full folder name, since one room can have several Spatiallm1_* 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	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, run with the fork 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	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	RGB-D mapping, textured mesh	P64/RTABMAP1/*.obj/.mtl/.jpg

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.*).

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 Spatiallm1_* 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 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 as up (0.085 raw height, scale x30). Only seed 0 found walls.
  • I87X/Spatiallm1_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..4: defaults, run directly with commit 7021237. Each covers the scanned part of the room as one rectangle (4 walls), except Spatiallm1_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).

• Metascenes1_* (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/Metascenes1_Spatiallm1_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/Metascenes1_Spatiallm1_MasterSlam2: 1 SpatialLM box, 1 after de-duplication (curtain); largest move in physics 0.00 m.
  • I12X/Metascenes1_Spatiallm1_MasterSlam3: 11 SpatialLM boxes, 11 after de-duplication (7 curtain, painting, 3 chandelier); largest move in physics 0.00 m.
  • I12X/Metascenes1_Spatiallm1_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.