ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage. In the semantic segmentation task, this dataset is marked in 20 classes of annotated 3D voxelized objects.
1,239 PAPERS • 19 BENCHMARKS
The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features:
839 PAPERS • 20 BENCHMARKS
The SUN RGBD dataset contains 10335 real RGB-D images of room scenes. Each RGB image has a corresponding depth and segmentation map. As many as 700 object categories are labeled. The training and testing sets contain 5285 and 5050 images, respectively.
422 PAPERS • 13 BENCHMARKS
UAVid is a high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation. The UAV dataset consists of 30 video sequences capturing 4K high-resolution images in slanted views. In total, 300 images have been densely labeled with 8 classes for the semantic labeling task.
34 PAPERS • 2 BENCHMARKS
StreetHazards is a synthetic dataset for anomaly detection, created by inserting a diverse array of foreign objects into driving scenes and re-render the scenes with these novel objects.
17 PAPERS • 1 BENCHMARK
Mila Simulated Floods Dataset is a 1.5 square km virtual world using the Unity3D game engine including urban, suburban and rural areas.
2 PAPERS • 1 BENCHMARK
A dataset comprised of real driving videos and GPS/IMU data. The BDDV dataset contains diverse driving scenarios including cities, highways, towns, and rural areas in several major cities in US.
1 PAPER • NO BENCHMARKS YET