The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers.
244 PAPERS • 10 BENCHMARKS
The Stanford 3D Indoor Scene Dataset (S3DIS) dataset contains 6 large-scale indoor areas with 271 rooms. Each point in the scene point cloud is annotated with one of the 13 semantic categories.
143 PAPERS • 4 BENCHMARKS
SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing.
101 PAPERS • 4 BENCHMARKS
PartNet is a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information. The dataset consists of 573,585 part instances over 26,671 3D models covering 24 object categories. This dataset enables and serves as a catalyst for many tasks such as shape analysis, dynamic 3D scene modeling and simulation, affordance analysis, and others.
48 PAPERS • 1 BENCHMARK
The SemanticPOSS dataset for 3D semantic segmentation contains 2988 various and complicated LiDAR scans with large quantity of dynamic instances. The data is collected in Peking University and uses the same data format as SemanticKITTI.
9 PAPERS • NO BENCHMARKS YET
The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).
7 PAPERS • 1 BENCHMARK
RELLIS-3D is a multi-modal dataset for off-road robotics. It was collected in an off-road environment containing annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. The dataset also provides full-stack sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurement, and IMU data.
6 PAPERS • 2 BENCHMARKS
A novel dataset and benchmark, which features 1482 RGB-D scans of 478 environments across multiple time steps. Each scene includes several objects whose positions change over time, together with ground truth annotations of object instances and their respective 6DoF mappings among re-scans.
3 PAPERS • NO BENCHMARKS YET
The platelet-em dataset contains two 3D scanning electron microscope (EM) images of human platelets, as well as instance and semantic segmentations of those two image volumes. This data has been reviewed by NIBIB, contains no PII or PHI, and is cleared for public release. All files use a multipage uint16 TIF format. A 3D image with size [Z, X, Y] is saved as Z pages of size [X, Y]. Image voxels are approximately 40x10x10 nm
2 PAPERS • 2 BENCHMARKS