mikasazgx

mikasazgx's Stars

• microsoft/Swin-Transformer

  This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

  Language:Python13.8k1263142k

• DayBreak-u/chineseocr_lite

  超轻量级中文ocr，支持竖排文字识别, 支持ncnn、mnn、tnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M

  Language:C++11.8k2423652.3k

• yenchenlin/nerf-pytorch

  A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

  Language:Python5.5k521151.1k

• open-mmlab/mmdetection3d

  OpenMMLab's next-generation platform for general 3D object detection.

  Language:Python5.3k611.6k1.5k

• shouxieai/tensorRT_Pro

  C++ library based on tensorrt integration

  Language:C++2.6k34215549

• PJLab-ADG/SensorsCalibration

  OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

  Language:C++2.4k47164571

• ankitdhall/lidar_camera_calibration

  ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

  Language:C++1.5k36137461

• CaoWGG/TensorRT-CenterNet

  tensorrt5 , centernet , centerface, deform conv, int8

  Language:C++76831105158

• astra-vision/MonoScene

  [CVPR 2022] "MonoScene: Monocular 3D Semantic Scene Completion": 3D Semantic Occupancy Prediction from a single image

  Language:Python693129867

• HKUST-Aerial-Robotics/Stereo-RCNN

  Code for 'Stereo R-CNN based 3D Object Detection for Autonomous Driving' (CVPR 2019)

  Language:Python6892784177

• PaddlePaddle/Paddle3D

  A 3D computer vision development toolkit based on PaddlePaddle. It supports point-cloud object detection, segmentation, and monocular 3D object detection models.

  Language:Python56818201141

• erdos-project/pylot

  Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.

  Language:Python46716114132

• maudzung/YOLO3D-YOLOv4-PyTorch

  YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (ECCV 2018)

  Language:Python28911544

• wufan-tb/AutoLabelImg

  auto-labelimg based on yolov5, with many other useful tools

  Language:Python24122356

• weixu000/libtorch-yolov3-deepsort

  Language:C++237113477

• tz28/deep-learning

  personal practice（个人练习，实现了深度学习中的一些算法，包括：四种初始化方法（zero initialize, random initialize, xavier initialize, he initialize），深度神经网络，正则化，dropout， 三种梯度下降方法(BGD, SGD, mini-batch)，六种优化算法(momentum、nesterov momentum、Adagrad、Adadelta、RMSprop、Adam)，梯度检验、batch normalization）、RNN

  Language:Python21660104

• liuchangji/kalman-filter-in-single-object-tracking

  A simple implementation of Kalman filter in single object tracking

  Language:Python2102659

• manila95/monolayout

  MonoLayout: Amodal Scene Layout from a single image

  Language:Python199111932

• tkwoo/StereoVisionforADAS

  Stereo vision based object detection for ADAS, autonomous vehicle

  Language:C++18427375

• IshitaTakeshi/PCANet

  A Python implementation of PCANet https://arxiv.org/abs/1404.3606

  Language:Python147141166

• SPengLiang/DID-M3D

  [ECCV 2022] DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection.

  Language:Python7623511

• Qjizhi/TensorRT-CenterNet-3D

  tensorrt5, centernet, 3d detection, kitti, centerface, deform conv, int8,

  Language:C++612514

• yanglei18/Awesome-Occupancy-Network

  Awesome papers and codebases about Occupancy Network in Tesla AI Day 2022

  24206

• GCaptainNemo/Lidar-Camera-Calibration-Software

  Lidar Camera Manual Target-less Calibration Software

  Language:Python18204

• GCaptainNemo/Lidar-Camera-Calibration

  The internal parameters and distortion parameters of the camera are known, and the external parameters between the lidar and the camera are calibrated.

  Language:Python4100

• ActiveIntelligentSystemsLab/moving_object_detector

  Moving objects detection from stereo images for ROS

  Language:C++2202

• qixuxiang/YOLO3D

  YOLO 3D Object Detection for Autonomous Driving Vehicle

  Language:Python2001

• mikasazgx/CUDA_Freshman

  Language:Cuda1000

• mikasazgx/deep-learning

  personal practice（个人练习，实现了深度学习中的一些算法，包括：四种初始化方法（zero initialize, random initialize, xavier initialize, he initialize），深度神经网络，正则化，dropout， 三种梯度下降方法(BGD, SGD, mini-batch)，六种优化算法(momentum、nesterov momentum、Adagrad、Adadelta、RMSprop、Adam)，梯度检验、batch normalization）、RNN

  Language:Python10

• mikasazgx/ORB_SLAM2

  Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

  Language:C++1000