50 datasets found

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

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen...

Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a...

The dataset used in the paper is a set of point clouds generated by ORB-SLAM3, a state-of-the-art vision feature-based SLAM system.

ModelNet is a large-scale 3D object recognition dataset containing 30,000 models from 50 categories.

The dataset used in the paper is ShapeNetPart, a synthetic dataset for 3D object part segmentation. It contains 16,881 models from 16 categories.

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 registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose...