There are 7 repositories under depth topic.
A complete, ready to use, Neumorphic ui kit for Flutter, 🕶️ dark mode compatible
[ECCV 2022] SimpleRecon: 3D Reconstruction Without 3D Convolutions
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
Pytorch version of SfmLearner from Tinghui Zhou et al.
Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation
[TPAMI'23] Unifying Flow, Stereo and Depth Estimation
Monocular Depth Estimation Toolbox based on MMSegmentation.
ARCore Depth Lab is a set of Depth API samples that provides assets using depth for advanced geometry-aware features in AR interaction and rendering. (UIST 2020)
SOS IROS 2018 GOOGLE; StereoNet ECCV2018 GOOGLE; ActiveStereoNet ECCV2018 Oral GOOGLE; HITNET CVPR2021 GOOGLE;PLUME Uber ATG
official implementation of "Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries"
Pulls together list of crypto exchanges to interact with their API's in a uniform fashion.
[MIR-2023-Survey] A continuously updated paper list for multi-modal pre-trained big models
Wow effect, transform your layout into 3D views
Light-field imaging application for plenoptic cameras
Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
[CVPR2020] BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion
Pytorch implementation of ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume"
clothes research in 3D
Uses matrix3d perspective distortions to create 3d scenes in the browser. Inspired by HelloMonday
ONNX-compatible Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
This repository contains information for the paper "A Survey on RGB-D Datasets" and is a collaborative initiative to update the datasets list faster.
3D face modeling and recognition using a depth camera (RGBD)
ROS node for real-time FCNN depth reconstruction
Code for 'Segment-based Disparity Refinement with Occlusion Handling for Stereo Matching'
这是一个基于CUDA加速的快速立体匹配库,它的核心是SemiglobalMatching(SGM)算法,它不仅在时间效率上要远远优于基于CPU的常规SGM,而且占用明显更少的内存,这意味着它不仅可以在较低分辨率(百万级)图像上达到实时的帧率,且完全具备处理千万级甚至更高量级图像的能力。