https://github.com/eaphan/upidet
공식 코드Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
Papers with Code 아카이브
3D Object Detection
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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/easywalk-prin/opennav
공식 코드OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation
https://github.com/eduardohenriquearnold/coop-3dod-infra
공식 코드Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors
https://github.com/ekut-es/mr3d-net
공식 코드MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception
https://github.com/elvintanhust/monocd
공식 코드MonoCD: Monocular 3D Object Detection with Complementary Depths
https://github.com/emecercelik/Temp-Frustum-Net
공식 코드https://github.com/emecercelik/ssl-3d-detection
공식 코드3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
https://github.com/envision-research/generalizable-bev
공식 코드Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
https://github.com/envision-research/scale-bev
공식 코드Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
https://github.com/erbloo/raan
공식 코드https://github.com/ericyi/GSPN
공식 코드GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
https://github.com/eth-pbl/cr3dt
공식 코드https://github.com/exiawsh/streampetr
공식 코드Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
https://github.com/fabiencode/lg3d
공식 코드https://github.com/facebookresearch/efm3d
공식 코드EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models
https://github.com/facebookresearch/imvotenet
공식 코드ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
https://github.com/facebookresearch/nerf-det
공식 코드NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
https://github.com/facebookresearch/omni3d
공식 코드Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
https://github.com/facebookresearch/projectaria_tools
공식 코드Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
https://github.com/facebookresearch/votenet
공식 코드https://github.com/fcjian/AeDet
공식 코드https://github.com/feeling0414-lab/V2X-AHD
공식 코드https://github.com/fengzicai/s2s
공식 코드Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data
https://github.com/fengziyue/FusionDepth
공식 코드Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
https://github.com/filapro/unidet3d
공식 코드https://github.com/fishsoup0/autonomous-driving-perception
공식 코드https://github.com/fnozarian/ReliableStudent
공식 코드Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection
https://github.com/frankfengdi/LidarMTL
공식 코드A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
https://github.com/fregu856/ebms_3dod
공식 코드https://github.com/fudan-zvg/DDMP
공식 코드Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
https://github.com/fudan-zvg/deepinteraction
공식 코드DeepInteraction: 3D Object Detection via Modality Interaction
https://github.com/fudan-zvg/ego3rt
공식 코드https://github.com/fudan-zvg/partner
공식 코드PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection
https://github.com/fudan-zvg/polarformer
공식 코드PolarFormer: Multi-camera 3D Object Detection with Polar Transformer
https://github.com/galaxy-zrx/edgehead
공식 코드https://github.com/ganwanshui/es6d
공식 코드ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.