https://github.com/MindSpore-scientific/code-8/tree/main/PolyLoss
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
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/ModelBunker/PointNet-TensorFlow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/ModelBunker/PointRCNN-PyTorch
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
https://github.com/ModelBunker/Stereo-RCNN-PyTorch
Stereo R-CNN based 3D Object Detection for Autonomous Driving
https://github.com/MohamedAbd0/darkflow-training
https://github.com/NANSHANB/StereoR-CNN
Stereo R-CNN based 3D Object Detection for Autonomous Driving
https://github.com/NVIDIA/MinkowskiEngine
Generative Sparse Detection Networks for 3D Single-shot Object Detection
https://github.com/Nicholasli1995/EgoNet
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/Nikhil619/Autonomous-driving-with-camera-using-Yolo
https://github.com/NonMagneticNeedle/vision.ai
https://github.com/Omar-Awad/SCNN_GPU2
https://github.com/OpenSpaceAI/DEST3D
State Space Model Meets Transformer: A New Paradigm for 3D Object Detection
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PericlesPet/darknet-demo
https://github.com/PierrickPochelu/word_tree_label
https://github.com/PingoLH/CenterNet-HarDNet
https://github.com/Pointcept/Pointcept
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/PointsCoder/ONCE_Benchmark
https://github.com/PointsCoder/Pyramid-RCNN
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
https://github.com/PointsCoder/VOTR
https://github.com/PolarisAlpha/darknet
https://github.com/Popoooo/darknet_test
https://github.com/Poseidon0711/Autonomous-driving-application---Car-detection
https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe
https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4
https://github.com/RACHANAMOVVA09/CenterNet
https://github.com/RPFey/frustum-pointnets
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov4
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4
https://github.com/RiplleYang/DenseFusion
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/RishiTejaMadduri/VoxelNet_PyTorch
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Ritik-Sharma38/YoloV3_training_Hands
https://github.com/RobbertBrand/Yolo-Tensorflow-Implementation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.