54 datasets found

OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas.

Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years. Panorama images maintain the complete spatial information but introduce distortion...

A dataset and evaluation methodology for depth estimation on 4D light fields.

The dataset used in this paper is an ICL-NUIM dataset, which contains images and depth maps.

The NYU-Depth V2 dataset contains pairs of RGB and depth images collected from Microsoft Kinect in 464 indoor scenes.

DepthP+P: A method for self-supervised monocular depth estimation using planar and parallax.

KeystoneDepth is a collection of over 10,000 rectified antique stereographs of historical scenes captured between 1860 and 1963.

A custom dataset created by rendering RGB-D images using perspective projection from high-quality 3D models of people.

MonoDiffusion: A novel self-supervised monocular depth estimation framework by reformulating it as an iterative denoising process.

Monocular depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment.

RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoT

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

The dataset used in the paper is a synthetic dataset for unsupervised depth estimation, which includes vKitti2, SHIFT, and UrbanSyn datasets.

KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240.

The ClearPose dataset consists of 63 objects arranged in numerous configurations.

The dataset is used for depth auto-encoding and contains depth images.

A reference model for depth estimation, used for evaluating the performance of the LDM3D model.

A dataset of images used for training and testing the DPT-Large depth estimation model.