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DISCOVERSE 3DGS scenes

How to fetch and render the DISCOVERSE indoor 3DGS captures used by the multi-camera AndroidTwin demo (see the showcase section of the top-level README).

No scenes are tracked in this repo — assets/scenes/*.ply is .gitignored. The download script is the only path.

What you get

Four indoor 3DGS captures from the DISCOVERSE project:

Scene Description Packed (HF) Unpacked
lab3 Robotics lab corner (cabinet, desk) ~5.5 MB ~23 MB
flower_table Indoor scene with a flower table ~7.9 MB ~33 MB
discover_operation_studio Operation studio room ~8.4 MB ~36 MB
tsimf_library_0 Library reading area ~22 MB ~92 MB

Source: tatp/DISCOVERSE-models on Hugging Face, paths under 3dgs/scene/<name>/point_cloud.ply.

Why the unpack step

DISCOVERSE ships scenes in PlayCanvas SuperSplat compressed PLY format (chunked uint32 packed fields), not the official 3DGS schema. MuGS's GaussianSensor only consumes the unpacked layout (x/y/z, f_dc_*, scale_*, rot_*, opacity), so the script runs decompress_supersplat.py once per scene right after download.

Quick start

# default: lab3 only
bash scripts/data_collection/download_discoverse_scenes.sh

# everything
bash scripts/data_collection/download_discoverse_scenes.sh --all

# pick a subset
bash scripts/data_collection/download_discoverse_scenes.sh lab3 flower_table

Outputs land in:

assets/scenes/discoverse/<scene>/point_cloud.ply          # packed (archival)
assets/scenes/discoverse_unpacked/<scene>/point_cloud.ply # unpacked (renderable)

Override with --dst-root <dir> or env DISCOVERSE_SCENE_ROOT=…. The unpacked directory always appends _unpacked to the chosen root.

Dependencies

  • curl (or wget) for download.
  • python3 with numpy and plyfile for the decompressor.

Render with MuGS

from mugs.sensors import GaussianSensor, GaussianSensorConfig

cfg = GaussianSensorConfig(
    width=480,
    height=360,
    background_ply_path="assets/scenes/discoverse_unpacked/lab3/point_cloud.ply",
    render_mode="3dgs_only",   # or "hybrid" with a MuJoCo foreground
)
sensor = GaussianSensor(cfg)

Pair with MuGSRecorder (in the AndroidTwin repo) for a full hybrid 3DGS + MuJoCo + MuJoCo-cam-tracking pipeline.

Gotchas worth knowing once

  1. Don't render the packed PLY directly. GaussianSensor will load it and silently render noise. Always go through the unpack step.
  2. Use bbox.center for xy, percentile(z, 5) for floor. COLMAP leaves a small number of sub-floor splats that drag bbox.min[2] ~1 m below the actual floor; the 5th-percentile reading is robust. The median xy is biased toward the densest splat cluster (e.g. lab3 median_y=+0.72 vs bbox_center_y=+0.20) which can push virtual cameras outside the training-cam convex hull — use the bbox center.
  3. gs_intrinsics ≠ MuJoCo intrinsics. Feed the GS scene's training focal length (lab3 ≈ 380 px at 480×360) to the renderer, not the MuJoCo camera's own fx/fy. A mismatch shows up as squashed or stretched backgrounds.
  4. GS coverage ≠ scene bbox. Each capture only renders cleanly inside the convex hull of the training cameras. lab3's training cams are biased toward the GS -X half — virtual cameras placed in the +X half will render dim/blurry noise. Use AndroidTwin's examples/dump_cam_params.py to isolate a single camera's GS-only render before composing a full multi-cam rollout.