https://github.com/microsoft/splineposenc
공식 코드Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields
Papers with Code 아카이브
3D Shape Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision , ECCV'20
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mkocabas/VIBE
공식 코드VIBE: Video Inference for Human Body Pose and Shape Estimation
https://github.com/myavartanoo/3DIAS_PyTorch
공식 코드3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces
https://github.com/ngailapdi/CLRec
공식 코드The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
https://github.com/nitinagarwal/GAMesh
공식 코드GAMesh: Guided and Augmented Meshing for Deep Point Networks
https://github.com/nvlabs/instant-ngp
공식 코드Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/nvlabs/tiny-cuda-nn
공식 코드Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/rehg-lab/3dshapegen
공식 코드https://github.com/rehg-lab/CLRec
공식 코드The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
https://github.com/rozentill/Front2Back
공식 코드Front2Back: Single View 3D Shape Reconstruction via Front to Back Prediction
https://github.com/samaonline/3D-Shape-Reconstruction-from-Free-Hand-Sketches
공식 코드https://github.com/sw-gong/spiralnet_plus
공식 코드SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
https://github.com/ubc-vision/nffb
공식 코드https://github.com/uncbiag/naisr
공식 코드NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
https://github.com/xingangpan/shadegan
공식 코드A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
https://github.com/xucao-42/mvas
공식 코드https://github.com/xuxy09/RSC-Net
공식 코드3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
https://github.com/ycjungsubhuman/density-adaptation
공식 코드Mesh Density Adaptation for Template-based Shape Reconstruction
https://github.com/yinyunie/Total3DUnderstanding
공식 코드https://github.com/yixin26/SVR-SP
공식 코드Neural Implicit 3D Shapes from Single Images with Spatial Patterns
https://github.com/zerchen/gSDF
공식 코드gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction
https://github.com/zubair-irshad/shapo
공식 코드ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
https://github.com/zxhuang1698/ZeroShape
공식 코드https://github.com/EnvisageIITM/HoloJest
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
https://github.com/Gitikameher/Domain-Adaptive-Single-View-3D-Reconstruction
https://github.com/IsaacRe/dynamic-representation-tracking
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
https://github.com/JeremyFisher/deep_level_sets
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
https://github.com/JeremyFisher/few_shot_3dr
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
https://github.com/Jittor/JNeRF
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/JonathonLuiten/instant-nerf-pytorch
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/JuliaInv/ParamLevelSet.jl
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
https://github.com/LONG-9621/SketchTo3D
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
https://github.com/Linyou/taichi-ngp-renderer
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/MChaus/NeoRender_test_task
AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Instant_Neural_Graphics_Primitives_with_a_Multiresolution_Hash_Encoding
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/aghinsa/SketchTo3D
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.