File size: 3,037 Bytes
af1dd73 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # 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
```bash
# 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`.
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