54 datasets found

Groups: Depth Estimation Organizations: No Organization

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  • NYUv2

    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...

  • Virtual Kitti RGB

    The dataset used in the paper is a spike dataset generated from RGB frames of four open access outdoor datasets, including Kitti, Driving Stereo, Driving Stereo Weather, and...

  • Driving Stereo Weather

    The dataset used in the paper is a spike dataset generated from RGB frames of four open access outdoor datasets, including Kitti, Driving Stereo, Driving Stereo Weather, and...

  • Driving Stereo

    The dataset used in the paper is a spike dataset generated from RGB frames of four open access outdoor datasets, including Kitti, Driving Stereo, Driving Stereo Weather, and...

  • Spike dataset

    The dataset used in the paper is a spike dataset generated from RGB frames of four open access outdoor datasets, including Kitti, Driving Stereo, Driving Stereo Weather, and...

  • Make3D

    The Make3D dataset is a small but challenging dataset for 3D object recognition and scene understanding, which consists of 134 images with aligned depth information.

  • Joint Prediction of Monocular Depth and Structure using Planar and Parallax G...

    The dataset used in the paper is the KITTI Vision Benchmark and Cityscapes dataset for monocular depth estimation and structure prediction.

  • NYUDv2

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

  • SUN RGB-D

    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...

  • MegaDepth

    Feature matching is a fundamental problem for many computer vision tasks, such as object recognition, structure from motion, and simultaneous localization and mapping.

  • Cityscapes

    The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...

  • KITTI dataset

    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...

  • KITTI 2015

    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.

  • Scene Flow

    Stereo matching aims to recover the dense reconstruction of unknown scenes by computing the disparity from rectified stereo images, helping robots intelligently interact with...