https://github.com/fangchangma/self-supervised-depth-completion
공식 코드Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
Papers with Code 아카이브
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/fengziyue/FusionDepth
공식 코드Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
https://github.com/fregu856/evaluating_bdl
공식 코드Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
https://github.com/galaxies99/transcg
공식 코드https://github.com/gdaosu/vis2mesh
공식 코드https://github.com/hailanyi/virconv
공식 코드Virtual Sparse Convolution for Multimodal 3D Object Detection
https://github.com/hekmak/Conf-net
공식 코드Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction
https://github.com/imransai/TWISE
공식 코드Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries
https://github.com/isl-org/vi-depth
공식 코드https://github.com/jamesqian11/sdformer-for-depth-completion
공식 코드SDformer: Efficient End-to-End Transformer for Depth Completion
https://github.com/jeongyun0609/TranSplat
공식 코드TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation
https://github.com/juanb09111/finnforest
공식 코드https://github.com/juanb09111/pandepth
공식 코드https://github.com/juanb09111/semantic_depth
공식 코드SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion
https://github.com/jyjunmcl/mspn_sdr
공식 코드Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement
https://github.com/kailaisun/indoor-depth-completion
공식 코드A Two-Stage Masked Autoencoder Based Network for Indoor Depth Completion
https://github.com/kakaxi314/GuideNet
공식 코드https://github.com/kakaxi314/bp-net
공식 코드https://github.com/kujason/ip_basic
공식 코드In Defense of Classical Image Processing: Fast Depth Completion on the CPU
https://github.com/kylelo/roofdiffusion
공식 코드RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion
https://github.com/lan1eve/svdc
공식 코드SVDC: Consistent Direct Time-of-Flight Video Depth Completion with Frequency Selective Fusion
https://github.com/lmomoy/cenet
공식 코드A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
https://github.com/lmomoy/chnet
공식 코드A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
https://github.com/megvii-research/sparse-beats-dense
공식 코드Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
https://github.com/mipi-challenge/mipi2022
공식 코드MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report
https://github.com/nonmy/fdct
공식 코드https://github.com/opipari/clearpose
공식 코드ClearPose: Large-scale Transparent Object Dataset and Benchmark
https://github.com/orbis36/wysiwyd
공식 코드What You See Is What You Detect: Towards better Object Densification in 3D detection
https://github.com/patrickwu2/Depth-Completion
공식 코드Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/placeforyiming/RAL_Non-Learning_DepthCompletion
공식 코드https://github.com/princeton-vl/ogni-dc
공식 코드OGNI-DC: Robust Depth Completion with Optimization-Guided Neural Iterations
https://github.com/princeton-vl/omni-dc
공식 코드OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration
https://github.com/seobbro/tta-depth-completion
공식 코드https://github.com/shacharp/Adaptive-LiDAR-Sampling
공식 코드Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance
https://github.com/shadowbbbb/depthor
공식 코드DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image
https://github.com/sshan-zhao/ACMNet
공식 코드Adaptive Context-Aware Multi-Modal Network for Depth Completion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.