Point cloud
PointCloudCorruptionPointCloudPeople
Dataset Card for PointCloudPeople
The use of light detection and ranging (LiDAR) sensor technology for people detection offers a significant advantage in terms of data protection. However, to design
these systems cost- and energy-efficiently, the relationship between the measurement data and final object detection output with deep neural networks (DNNs) has to be
elaborated. Therefore, we present an automatically labeled LiDAR dataset for person detection, with different… See the full description on the dataset page: https://huggingface.co/datasets/LukasPro/PointCloudPeople.PointCloud-groundingDrivaerml_point_clouds
DrivAerML Point Clouds
A preprocessed, point-cloud version of the DrivAerML high-fidelity CFD dataset, ready for training point-based deep learning surrogates (PointNet, PCT, DGCNN, Graph Neural Operators, etc.) for automotive external aerodynamics.
The original DrivAerML release contains 500 scale-resolving CFD simulations of parametrically morphed DrivAer notchback geometries and ships as 31 TB of raw STL / VTP / VTU / OpenFOAM data. This release distills the surface boundary of… See the full description on the dataset page: https://huggingface.co/datasets/Jrhoss/Drivaerml_point_clouds.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.climbing-holds-pointcloud
Rock Climb — Grasp-Taxonomy-Aware 3D Diffusion Policy
Trains a DP3-style point cloud diffusion policy conditioned on grasp type (crimp/sloper/pinch/jug)
to autonomously grasp climbing holds with a Franka arm + LEAP Hand.
Quick Start (Training Machine)
1. Clone the repo
git clone https://github.com/rumilog/rock-climb.git tele
cd tele
2. Create a Python environment
python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
Install… See the full description on the dataset page: https://huggingface.co/datasets/rlogh/climbing-holds-pointcloud.