14 datasets found

3D semantic parsing of large-scale indoor spaces.

MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views

The Middlebury 2014 dataset is a benchmark for stereo matching, consisting of 33 pairs of stereo images with sparse depth ground truth.

The Scan2CAD dataset contains object-level human-generated annotations. The annotations include category label, segmentation, a similar CAD model in ShapeNet, and the...

SUN3D dataset is a large-scale dataset of RGB-D scans of indoor scenes.

Scannet: Richly-annotated 3D reconstructions of indoor scenes.

The 3DMatch [26] is a well-known indoor registration dataset of 62 scenes captured by RGBD sensor.

The dataset used in the paper is ScanNetV2, a real-world dataset for indoor scenes. It contains 1205 training scenes and 312 testing scenes, with instance-level object bounding...

The dataset used in the Point2Pix paper, containing point clouds and camera parameters for indoor scenes.

The Habitat dataset is a large-scale indoor simulator dataset containing 145 semantically-annotated indoor scenes.

3D-FRONT is a synthetic dataset composed of 6,813 houses with 14,629 rooms, where each room is arranged by a collection of high-quality 3D furniture objects from the 3D-FUTURE...

The dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.

The ScanNet dataset is a large-scale indoor dataset composed of monocular sequences with ground truth poses and depth images.

The dataset used in the paper is a real-world 3D point cloud dataset, which is used for 3D shape classification, part segmentation, and shape retrieval tasks.