8 datasets found

The NYUD-v2 dataset is a benchmark for indoor scene segmentation and depth estimation. It contains 1449 images with 4 tasks: semantic segmentation, depth estimation, surface...

Scenenet RGB-D: 5M photorealistic images of synthetic indoor trajectories with ground truth

NYUD2 dataset is a dataset containing RGB-D images for indoor scene understanding. It is used for depth estimation and semantic segmentation tasks.

The dataset used in this paper for multi-sensor next-best-view planning as matroid-constrained submodular maximization.

Multi-task learning (MTL) research is broadly divided into two categories: one is to learn the correlation between tasks through model structures, and the other is to balance...

The Replica dataset contains 18 various highly photo-realistic indoor environments. It provides dense-mesh, high-resolution RGBD images and a large range of instance annotations...

The NYUDv2 dataset contains 1,449 labeled indoor-scene RGB images with both parsing annotations and Kinect depths.

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...