Kit-Key/libero-3d-scene-pointcloud
LIBERO 3D Whole-Scene Point-Cloud GT (sim, labeled) Per-frame whole-scene 3D point cloud for the LIBERO benchmark, sampled directly from every object's visual mesh (posed by the replayed MuJoCo state) — complete, view-independent geometry. Every point carries an instance label, and a single is_ooi flag marks the task's objects-of-interest, so relevant objects are extracted with one key at training time. Scene = task objects + fixtures + the gripper (robot arm/mount excluded).… See the full description on the dataset page: https://huggingface.co/datasets/Kit-Key/libero-3d-scene-pointcloud.
LIBERO 3D Whole-Scene Point-Cloud GT (sim, labeled)
Per-frame whole-scene 3D point cloud for the LIBERO benchmark, sampled directly from every object's visual mesh (posed by the replayed MuJoCo state) — complete, view-independent geometry. Every point carries an instance label, and a single is_ooi flag marks the task's objects-of-interest, so relevant objects are extracted with one key at training time.
Scene = task objects + fixtures + the gripper (robot arm/mount excluded). 130 tasks × 50 demos = 6500 episodes, every frame.
Format (one .h5 per episode; per-frame group "%06d")
Each OOI instance is guaranteed ≥512 points; the rest of the 4096 budget is split area-weighted across the other instances.
ooi_points = scene_pc[is_ooi] # all key-object points (one key)
obj_points = scene_pc[point_inst == i] # a single instancePer-object variants:Kit-Key/libero-3d-pointcloud-complete(complete mesh),Kit-Key/libero-3d-pointcloud(single-view depth 2.5D).
