52 datasets found

The VoxelNet dataset is a large-scale dataset for 3D object detection, consisting of 3D point clouds and corresponding annotations.

The Multi-View 3D Object Detection Network is a dataset for 3D object detection, consisting of 3D point clouds and corresponding annotations.

The KITTI benchmark suite is a large-scale dataset for 3D object detection, consisting of 7,481 training samples and 7,518 test samples.

A dataset for 3D object detection from LiDAR point clouds, containing 5,000 training frames and 3,000 validation frames.

A dataset for 3D object detection from LiDAR point clouds, containing 7,481 labeled samples and 3,712 training samples and 3,769 validation samples.

A proposal-level point cloud SSL framework for 3D object detection, learning robust 3D representations by contrasting region proposals.

RGB-D scene recognition approaches often train two standalone backbones for RGB and depth modalities with the same Places or ImageNet pre-training. However, the pre-trained...

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.

The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...

Point cloud (PC) is a collection of points in 3D space, represented as P = {pk} = {(ck, rk)}, where ck = (xk, yk, zk) denotes the 3D coordinate of the k’th point, and rk is its...

3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and...

The KITTI 2015 dataset is a real-world dataset of street views, containing 200 training stereo image pairs with sparsely labeled disparity from LiDAR data.