Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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3D Reconstruction is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.
Image: Gwak et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sqyon/MDFN
공식 코드Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic Filters
https://github.com/starrah/dmh-net
공식 코드3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform
https://github.com/stevewongv/sparse2dense
공식 코드Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
https://github.com/sunjiayuanro/scfeat
공식 코드Shared Coupling-bridge for Weakly Supervised Local Feature Learning
https://github.com/sunset1995/HoHoNet
공식 코드HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
https://github.com/sunset1995/HorizonNet
공식 코드HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation
https://github.com/sunset1995/panoplane360
공식 코드Indoor Panorama Planar 3D Reconstruction via Divide and Conquer
https://github.com/surgical-vision/colmap-match-converter
공식 코드Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks
https://github.com/surgical-vision/servcttk
공식 코드SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic
공식 코드https://github.com/svddchallenge/ctrsvdd2024_baseline
공식 코드SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
https://github.com/svip-lab/PlanarReconstruction
공식 코드Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
https://github.com/swagshaw/xlsr-mamba
공식 코드XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection
https://github.com/sxyu/pixel-nerf
공식 코드https://github.com/szymanowiczs/splatter-image
공식 코드https://github.com/tabularaza27/ice_cloud_net
공식 코드IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI
https://github.com/taco-group/hfmf
공식 코드HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection
https://github.com/talkingnow/HM-Conformer
공식 코드https://github.com/talreiss/factor
공식 코드https://github.com/tamlhp/dfd_benchmark
공식 코드A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures
https://github.com/tangtaogo/lidar-nerf
공식 코드LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
https://github.com/tanqianq/enhance-nerf
공식 코드Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields
https://github.com/terry9a/dsrc
공식 코드https://github.com/tfy14esa/senfunet
공식 코드https://github.com/thanif/CPL
공식 코드https://github.com/thanif/Camera-Calibration-through-Camera-Projection-Loss
공식 코드https://github.com/thhhomas/tensorformer6
공식 코드Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction
https://github.com/thinkxca/3dgs
공식 코드3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene Reconstruction
https://github.com/thmoa/videoavatars
공식 코드https://github.com/thu-lyj-lab/o2-recon
공식 코드https://github.com/tianxinhuang/lcdnet
공식 코드https://github.com/tianyuan1wang/seqanglerl
공식 코드Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning
https://github.com/timduff35/PLMP
공식 코드PLMP -- Point-Line Minimal Problems in Complete Multi-View Visibility
https://github.com/tiongleslie/3D-C2FT
공식 코드3D-C2FT: Coarse-to-fine Transformer for Multi-view 3D Reconstruction
https://github.com/tizfa/tweepfake_deepfake_text_detection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.