Papers with Code 아카이브
3D Shape Representation
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Image: MeshNet
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ChengXu1995/test
Learning Discriminative 3D Shape Representations by View Discerning Networks
https://github.com/Colin97/OpenShape_code
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
https://github.com/Facebookresearch/deepsdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/Ouasfi/SparseOcc
https://github.com/PaulaMihalcea/Neural3DMM_BU3DFE
https://github.com/RobinBaumann/TF-AtlasNetV2
Learning elementary structures for 3D shape generation and matching
https://github.com/Seed3D/Dora
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
https://github.com/ThibaultGROUEIX/3D-CODED
https://github.com/ahnobari/Range-GAN
https://github.com/chengz3906/View-Discerning-Network
Learning Discriminative 3D Shape Representations by View Discerning Networks
https://github.com/christophreich1996/3d_baggage_segmentation
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/christophreich1996/oss-net
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/code-implementation1/Code9/tree/main/PVNet
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
https://github.com/crazyleg/workshop-3d-neural
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/cscvlab/VoxelReconstruction-NAS
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
https://github.com/czq142857/BSP-NET-pytorch
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
https://github.com/czq142857/im-net
https://github.com/czq142857/im-net-pytorch
https://github.com/devlearning-gt/3DShapeGen
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/google/ldif
https://github.com/hamidreza-dastmalchi/3dd-tta
https://github.com/iMoonLab/MeshNet
https://github.com/komeiharada/learn_DL_together
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
https://github.com/marian42/mesh_to_sdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/marian42/shapegan
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/maurock/deepsdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/mli0603/blenderneuralangelo
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
https://github.com/nathanrgodwin/overfit-shapes
On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes
https://github.com/raphaelsulzer/dsr-benchmark
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/raphaelsulzer/dsrv-data
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/rehg-lab/3dshapegen
Occupancy Networks: Learning 3D Reconstruction in Function Space
https://github.com/riccardomarin/diff-fmaps-pytorch
https://github.com/rpreen/superformula
Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes
https://github.com/sudhir5595/Mesh_Neural_Network
https://github.com/vita-group/ins
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.