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/maudzung/SFA3D
https://github.com/maudzung/super-fast-accurate-3d-object-detection
https://github.com/mdv3101/darknet-yolov3
https://github.com/megapose6d/megapose6d
MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare
https://github.com/meteahishali/srl-soa
Representation Based Regression for Object Distance Estimation
https://github.com/meteorshowers/X-StereoLab
https://github.com/mialbro/PointFusion
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
https://github.com/mialbro/pytorch_pointfusion
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
https://github.com/michhar/azureml-keras-yolov3-custom
https://github.com/mindspore-ai/models/tree/master/research/cv/centernet_resnet50_v1
https://github.com/minhdua/PHONES
https://github.com/mit-han-lab/sparsevit
SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer
https://github.com/mit-han-lab/torchsparse
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/mjseong0414/TANet
TANet: Robust 3D Object Detection from Point Clouds with Triple Attention
https://github.com/mon95/centerpoint-maps
https://github.com/mshn2000/PointFusion
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
https://github.com/nachiket273/loss-tryout
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
https://github.com/native2019/hello
https://github.com/naviocean/CenterNet
https://github.com/nerminsamet/houghnet-vid
HoughNet: Integrating near and long-range evidence for visual detection
https://github.com/ngowilliam1/centerfusion_depth_factor
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
https://github.com/nguoido/Yolo-alexeyAB
https://github.com/nuaaceieyty/Paddle-YOLOv2
https://github.com/nvidia-ai-iot/lidar_ai_solution
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/nweakly/MSDSProject-II
https://github.com/ohshyuk5/Memory-based-Exchangeable-Model
Learning Permutation Invariant Representations using Memory Networks
https://github.com/oneformer3d/oneformer3d
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
https://github.com/oorrppp2/darknet-pose
https://github.com/open-mmlab/mmdetection
https://github.com/opengvlab/internimage
https://github.com/osmr/imgclsmob
https://github.com/parth-shastri/YOLOv2
https://github.com/patelmiteshn/darknet
https://github.com/peter8301120/darknet_t
https://github.com/pierrebaque/DeepOcclusion
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.