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Depth Completion
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The Depth Completion task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.
Source: LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery , Unsupervised Depth Completion from Visual Inertial Odometry
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tsunghan-wu/depth-completion
공식 코드Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/url-kaist/Struct-MDC
공식 코드https://github.com/vita-epfl/Helvipad
공식 코드Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
https://github.com/wang-xjtu/g2-monodepth
공식 코드G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data
https://github.com/wbkit/reslan
공식 코드https://github.com/whu-usi3dv/sparsedc
공식 코드SparseDC: Depth Completion from sparse and non-uniform inputs
https://github.com/whu-usi3dv/whu-synthetic
공식 코드WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Research
https://github.com/wvangansbeke/Sparse-Depth-Completion
공식 코드Sparse and noisy LiDAR completion with RGB guidance and uncertainty
https://github.com/xianghuifan/tdcnet
공식 코드TDCNet: Transparent Objects Depth Completion with CNN-Transformer Dual-Branch Parallel Network
https://github.com/xinliu20/graphcspn_eccv2022
공식 코드https://github.com/yanzq95/ldcnet
공식 코드Learnable Differencing Center for Nighttime Depth Perception
https://github.com/youmi-zym/completionformer
공식 코드CompletionFormer: Depth Completion with Convolutions and Vision Transformers
https://github.com/yvanyin/filldepth
공식 코드https://github.com/zzangjinsun/NLSPN_ECCV20
공식 코드https://github.com/2023-MindSpore-1/ms-code-172
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
https://github.com/AvrilCheng/LidarStereoNet
https://github.com/EbadSyed/spadRGBD
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
https://github.com/GreatV/PP-GuideNet
https://github.com/JiaxiongQ/DeepLiDAR
https://github.com/LakshmiTeja17/NNFL-Project
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
https://github.com/LeonSun0101/CD-SD
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
https://github.com/PeterTor/sparse_convolution
https://github.com/alexklwong/void-dataset
https://github.com/andreaconti/ramdepth
https://github.com/denyingmxd/selftof
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature Propagation
https://github.com/fangchangma/sparse-to-dense.pytorch
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
https://github.com/kairenye/LidarStereoFusion
https://github.com/katieluo88/280finalproj_nyudepth
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
https://github.com/likesum/prdepth
Generating and Exploiting Probabilistic Monocular Depth Estimates
https://github.com/sabadijou/FastDepthCompletionCPU
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
https://github.com/tsunghan-mama/Depth-Completion
Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/yindaz/DeepCompletionRelease
https://github.com/zswang666/Stereo-LiDAR-CCVNorm
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.