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3d-boundingbox

PyTorch implementation for 3D Bounding Box Estimation Using Deep Learning and Geometry

3d-deepbox

3D Bounding Box Estimation Using Deep Learning and Geometry (MultiBin)

3d_multiview_reg

Learning multiview 3D point cloud registration

ai_uav

在人工智能、机器视觉、高精度导航定位和多传感器融合等技术的助推下，众多行业迎来了前所未有的发展机遇，人工智能+无人机（AI+UAV）正是一个具有无限想象力的应用方向。杭州启飞智能科技有限公司深耕无人机及人工智能领域多年，拥有完善的软硬件平台、海量数据资源（飞行数据及农田地理信息数据）及众多资深专家团队，正在努力地开拓AI+UAV在科技+农业的行业应用，欢迎加入

breast-imaging-deeplearning

基于深度学习的乳腺医学诊断

cnn-visualization

CNN可视化、理解CNN

cvpr2018_attention

Context Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark

d2l-pytorch

This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.

d2l-zh

《动手学深度学习》，英文版即伯克利深度学习（STAT 157，2019春）教材。面向中文读者、能运行、可讨论。

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearningbook-chinese

Deep Learning Book Chinese Translation

deeplearningexamples

Deep Learning Examples

electronic-wechat

:speech_balloon: A better WeChat on macOS and Linux. Built with Electron by Zhongyi Tong.

frustum-pointnets

Frustum PointNets for 3D Object Detection from RGB-D Data

hithesis

嗨！thesis！哈尔滨工业大学毕业论文LaTeX模板

imu_utils

A ROS package tool to analyze the IMU performance.

in-prestissimo

A very fast neural network computing framework optimized for mobile platforms.QQ group: 676883532 【验证信息输：绝影】

interview_internal_reference

2019年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。

mace

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

mobile-deep-learning

mv3d

Multi-View 3D Object Detection Network for Autonomous Driving

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

paddle-mobile

This research aims at simply deploying deeplearning on mobile and embedded devices, with low complexity and high speed. old name mobile deep learning.

panet

PANet for Instance Segmentation and Object Detection

pocketflow

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

pytorch-yolo2

Convert https://pjreddie.com/darknet/yolo/ into pytorch

realtime_multi-person_pose_estimation

Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)