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3D geometry
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/machineperceptionlab/surface-sensing-sdf
공식 코드Sensing Surface Patches in Volume Rendering for Inferring Signed Distance Functions
https://github.com/marco-garosi/COPS
공식 코드3D Part Segmentation via Geometric Aggregation of 2D Visual Features
https://github.com/mbanani/novelviewpoints
공식 코드Novel Object Viewpoint Estimation through Reconstruction Alignment
https://github.com/megvii-research/occdepth
공식 코드OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion
https://github.com/mengyou2/pcvs
공식 코드Learning A Locally Unified 3D Point Cloud for View Synthesis
https://github.com/mengyuest/SIGNet
공식 코드SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
https://github.com/michaildoukas/head2head
공식 코드https://github.com/microsoft/S2R-DepthNet
공식 코드S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets
공식 코드Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
https://github.com/mrzihan/dynam3d
공식 코드Dynam3D: Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation
https://github.com/nadeemlab/cir
공식 코드https://github.com/nmwsharp/neural-implicit-queries
공식 코드Spelunking the Deep: Guaranteed Queries on General Neural Implicit Surfaces via Range Analysis
https://github.com/notacracker/cotr
공식 코드COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction
https://github.com/nrel/nfp
공식 코드Message-passing neural networks for high-throughput polymer screening
https://github.com/nv-nguyen/pizza
공식 코드PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking
https://github.com/nvlabs/voxformer
공식 코드VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
https://github.com/open-mmlab/mmdetection3d
공식 코드NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
https://github.com/opendrivelab/vidar
공식 코드Visual Point Cloud Forecasting enables Scalable Autonomous Driving
https://github.com/openrobotlab/vlm-grounder
공식 코드https://github.com/oppo-us-research/planemvs
공식 코드https://github.com/paschalidoud/hierarchical_primitives
공식 코드Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
https://github.com/pavlo-melnyk/mlgp-embedme
공식 코드https://github.com/pavlo-melnyk/steerable-3d-neurons
공식 코드https://github.com/pierreoo/morphoskel3d
공식 코드https://github.com/ppliuboy/Flow2Stereo
공식 코드Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching
https://github.com/pramishp/ihuman
공식 코드iHuman: Instant Animatable Digital Humans From Monocular Videos
https://github.com/qianmingduowan/Sat2Density
공식 코드Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
https://github.com/qizekun/ShapeLLM
공식 코드ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/radekd91/emoca
공식 코드https://github.com/schindlerepfl/thermo-nerf
공식 코드https://github.com/shaharzuler/CardioSpectrum
공식 코드https://github.com/shawn615/FlipNeRF
공식 코드FlipNeRF: Flipped Reflection Rays for Few-shot Novel View Synthesis
https://github.com/shenzheng2000/pointnorm-for-point-cloud-analysis
공식 코드PointNorm: Dual Normalization is All You Need for Point Cloud Analysis
https://github.com/shiqiyu/opengait
공식 코드LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
https://github.com/sicxu/Deep3dPortrait
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.