https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
Papers with Code 아카이브
3D Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 10
- 보존된 평가 기록
- 44
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/maurock/deepsdf
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
https://github.com/maurock/object_reconstruction
https://github.com/mees/self-supervised-3D
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics
https://github.com/melax/hand_tracking_samples
https://github.com/mindspore-ai/models/tree/master/research/cv/eppmvsnet
https://github.com/minxuanjun/basalt-mirror
https://github.com/minxuanjun/basalt_class
https://github.com/mit-spark/kimera-multi
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
https://github.com/mli0603/blenderneuralangelo
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
https://github.com/mlpc-ucsd/bdm
https://github.com/muskie82/monogs
https://github.com/nail-hnu/esvio_aa
https://github.com/nail-hnu/esvo2
https://github.com/natowi/3D-reconstruction-with-Neural-Networks
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
https://github.com/naver/kapture
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
https://github.com/nerfstudio-project/nerfstudio
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
https://github.com/neverUseThisName/Face-X-Ray
https://github.com/nianticlabs/simplerecon
https://github.com/nicksunday/deepfake-music-detector
https://github.com/nsinghal7/Kimera-VIO-NetVLAD
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/nsinghal7/Kimera-VIO-ROS-NetVLAD
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/nttcom/wasb-sbdt
MonoTrack: Shuttle trajectory reconstruction from monocular badminton video
https://github.com/nv-tlabs/XCube
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
https://github.com/nvidiagameworks/kaolin-wisp
https://github.com/nvlabs/umr
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
https://github.com/oloocki/scan2lod3
https://github.com/open-mmlab/mmdetection3d
Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction
https://github.com/paarthneekhara/adversarialdeepfakes
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
https://github.com/palver7/CFLPytorch
Corners for Layout: End-to-End Layout Recovery from 360 Images
https://github.com/palver7/EquiConvPytorch
Corners for Layout: End-to-End Layout Recovery from 360 Images
https://github.com/paul007pl/VRCNet
https://github.com/pbonazzi/guassian-diffusion
https://github.com/pfnet-research/distilled-feature-fields
https://github.com/piotrkawa/audio-deepfake-source-tracing
https://github.com/pothabattulasantosh/Detection-of-face-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.