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Point cloud reconstruction
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This task aims to solve inherent problems in raw point clouds: sparsity, noise, and irregularity.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LifeBeyondExpectations/PointMixer
공식 코드https://github.com/Qi-Yangsjtu/MPED
공식 코드MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy
https://github.com/SBCV/SatelliteSurfaceReconstruction
공식 코드https://github.com/SparklingH/BloomScene
공식 코드BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation
https://github.com/canonical-capsules/canonical-capsules
공식 코드Canonical Capsules: Self-Supervised Capsules in Canonical Pose
https://github.com/davzha/DESP
공식 코드Set Prediction without Imposing Structure as Conditional Density Estimation
https://github.com/diegovalsesia/xmfnet
공식 코드Cross-modal Learning for Image-Guided Point Cloud Shape Completion
https://github.com/facebookresearch/vggt
공식 코드https://github.com/fei960922/GPointNet
공식 코드https://github.com/hsgser/self-amortized-dsw
공식 코드https://github.com/i2-multimedia-lab/pcc_patch
공식 코드Patch-Based Deep Autoencoder for Point Cloud Geometry Compression
https://github.com/jinec98/mae3d
공식 코드Masked Autoencoders in 3D Point Cloud Representation Learning
https://github.com/johanye/vf-net
공식 코드https://github.com/khainb/ebsw
공식 코드https://github.com/lidq92/lsrn-pcgc
공식 코드Lightweight super resolution network for point cloud geometry compression
https://github.com/lifebeyondexpectations/eccv22-pointmixer
공식 코드https://github.com/lijx10/SO-Net
공식 코드https://github.com/liuquan98/apr
공식 코드APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction
https://github.com/lzqsd/TransparentShapeReconstruction
공식 코드Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
https://github.com/rhtm02/PU-MFA
공식 코드PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
https://github.com/shichengchen/multiviewdataset
공식 코드Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
https://github.com/stevewongv/sparse2dense
공식 코드Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
https://github.com/terry9a/dsrc
공식 코드https://github.com/thhhomas/tensorformer6
공식 코드Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction
https://github.com/tianxinhuang/lcdnet
공식 코드https://github.com/tyraeldlee/diffpmae
공식 코드DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstruction
https://github.com/val-iisc/3d-lmnet
공식 코드https://github.com/val-iisc/3d-psrnet
공식 코드3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
https://github.com/val-iisc/capnet
공식 코드CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
https://github.com/val-iisc/densepcr
공식 코드Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
https://github.com/val-iisc/ssl_3d_recon
공식 코드From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
https://github.com/vinay-lanka/ObitoNet
공식 코드ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction
https://github.com/whubaichuan/M3VSNet
공식 코드M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
https://github.com/ybzh/masksurf
공식 코드Masked Surfel Prediction for Self-Supervised Point Cloud Learning
https://github.com/yhw-yhw/D2HC-RMVSNet
공식 코드Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.