https://github.com/supermhp/gupnet
공식 코드Geometry Uncertainty Projection Network for Monocular 3D Object Detection
Papers with Code 아카이브
3D Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 67
- 보존된 평가 기록
- 806
3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: AVOD )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/supermhp/gupnet_plus
공식 코드GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection
https://github.com/sxjyjay/msmdfusion
공식 코드https://github.com/synsin0/effocc
공식 코드EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy Network
https://github.com/synsin0/srcn3d
공식 코드SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
https://github.com/syscv/lidar_snow_sim
공식 코드https://github.com/tai-wang/depth-from-motion
공식 코드https://github.com/tbd-clip/efficientpose
공식 코드https://github.com/tedlentsch/union
공식 코드UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
https://github.com/tensorflow/lingvo
공식 코드DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
https://github.com/terry9a/dsrc
공식 코드https://github.com/thangvubk/softgroup
공식 코드https://github.com/thu17cyz/3DIoUMatch
공식 코드3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
https://github.com/thusiyuan/cooperative_scene_parsing
공식 코드Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
https://github.com/tianweiy/CenterPoint
공식 코드https://github.com/tianweiy/MVP
공식 코드https://github.com/tiffamy/3dod-lsf
공식 코드Improving Generalization Ability for 3D Object Detection by Learning Sparsity-invariant Features
https://github.com/timbroed/hrfuser
공식 코드HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection
https://github.com/tingxueronghua/dpke
공식 코드Dual-Perspective Knowledge Enrichment for Semi-Supervised 3D Object Detection
https://github.com/tjiiv-cprg/MonoRUn
공식 코드MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation
https://github.com/tjiiv-cprg/epro-pnp
공식 코드https://github.com/tjiiv-cprg/epro-pnp-v2
공식 코드https://github.com/tjradarlab/tj4dradset
공식 코드https://github.com/tom-roddick/mono-semantic-maps
공식 코드Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks
https://github.com/tosshero/3d_perception
공식 코드Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction
https://github.com/tosshero/ros_packages
공식 코드Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction
https://github.com/towardsautonomy/VR3Dense
공식 코드https://github.com/towardsautonomy/datasetequity
공식 코드DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets
https://github.com/trailab/gmir
공식 코드Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection
https://github.com/trailab/hypermodest
공식 코드HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering
https://github.com/trailab/pdv
공식 코드https://github.com/tri-ml/dd3d
공식 코드https://github.com/tri-ml/vedet
공식 코드https://github.com/ttaoREtw/ImGeoNet
공식 코드ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection
https://github.com/tteepe/EarlyBird
공식 코드EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View
https://github.com/tteepe/tracktacular
공식 코드Lifting Multi-View Detection and Tracking to the Bird's Eye View
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.