PerceptPick — pre-prepared assets
This bundle holds the URDF / VHACD / mesh assets for the YCB-V dataset across nine mesh sources (oracle CAD plus eight reconstruction methods), together with FoundationPose and MegaPose pose-estimator CSVs.
Drop into a perceptpick clone to skip Stage A (01_prepare_assets.py)
and the FoundationPose / MegaPose pipelines:
git clone <perceptpick>
cd perceptpick
# 1. download the BOP YCBV test split (scenes 48-59 + models)
# see the README's "Get the YCB-Video dataset" section.
# 2. unpack this bundle next to the repo
unzip perceptpick_assets.zip
# 3. wire the bundle into the expected path
mkdir -p assets
mv perceptpick_assets/ycbv assets/ycbv
After that, jump straight to Stage B / C — no Stage A re-prep needed.
Layout
ycbv/
├── GT/ # oracle CAD (BOP YCBV models)
│ ├── meshes/obj_NNNNNN.{obj,mtl,png}
│ ├── vhacd/obj_NNNNNN_vhacd.obj
│ ├── urdf/obj_NNNNNN.urdf
│ └── pose_estimates/
│ ├── FoundationPose.csv # FoundationPose on GT meshes
│ └── MegaPose.csv # MegaPose on GT meshes
├── BakedSDF/ # 8 reconstruction methods
│ ├── meshes/, vhacd/, urdf/
│ └── pose_estimates/
│ ├── FoundationPose.csv # FoundationPose on BakedSDF
│ └── MegaPose.csv # MegaPose on BakedSDF
├── MonoSDF/, Nerfacto/, Neuralangelo/
├── NGP/, RealCAP/, UniSurf/, VolSDF/
Each method folder is fully self-contained: the meshes the simulator loads, the URDFs and VHACDs the physics layer needs, and the pose CSVs that were generated using that mesh as the pose-estimator's reference model. The CSVs are tiny; the meshes / VHACDs make up almost all of the disk footprint.
URDF paths
URDFs reference the sibling collision mesh with a relative path:
<mesh filename="../vhacd/obj_NNNNNN_vhacd.obj"/>. No absolute paths,
no system-specific roots — the bundle is portable.
Running the benchmark
# Stage B — sample antipodal grasps + simulate, per (object, gripper) on the GT meshes
pixi run python scripts/02_grasp_sweep.py --dataset ycbv --mesh-source GT --n-grasps 5000
# Stage C, Condition 1 — Oracle / Oracle (ideal baseline)
pixi run python scripts/04_evaluate.py --dataset ycbv \
--gt-mesh GT --est-mesh GT \
--pose-csv FoundationPose.csv --gripper auto --workers 4 --resume --headless
# Stage C, Condition 3 — End-to-end realistic (BakedSDF mesh + BakedSDF-conditioned pose)
pixi run python scripts/04_evaluate.py --dataset ycbv \
--gt-mesh BakedSDF --est-mesh BakedSDF \
--pose-csv FoundationPose.csv --gripper auto --workers 4 --resume --headless
If you'd rather regenerate the assets from scratch (e.g. to verify VHACD
parameters), ignore this bundle and run
scripts/01_prepare_assets.py --dataset ycbv --all-mesh-sources.