ewenwan Goto Github PK

rpi

树莓派+Arduino+TensorFlow：搭建图像识别小车

rtabmap

RTAB-MAP（Real Time Appearance-Based Mapping）是RGB-D SLAM中比较经典的一个方案

rtcprgb2gray

基于能量优化理论的彩色图像去色 An implementation of "Real-time Contrast Preserving Decolorization"

rtfsc_linux_kernel

USTC 嵌入式系统实验室2013级研究生阅读内核记录

ruima.me

个人独立博客，写点东西总结

rvaf

Robot Vision Algorithm Framework

sandbird

A tiny embeddable HTTP server written in C89

satellite-segmentation

scavislam

This is a general and scalable framework for visual SLAM. It employs "Double Window Optimization" (DWO).

scenelib2

MonoSLAM 单目 基于EKF形式的VSLAM

school

Believers in Science Studio 学习资料整理 repo

scikit-learn-doc-cn

scikit-learn机器学习库中文文档翻译项目

scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scipy-lecture-notes_cn

Pytho科学计算生态介绍的中文翻译，英文原文地址：

scnn

SCNN-用于时序动作定位的多阶段3D卷积网络

screader

The screader is a soure code reading tool based the libclang. It is implemented by the C.

screenly-ose

基于树莓派的 免费数字标牌软件 Screenly OSE | Digital Signage for the Raspberry Pi https://blog.just4fun.site/screenly-rpi.html

script_for_wq

基于加权熵对深度神经网络进行量化的方法

seayxu

second.pytorch

PointPillars for KITTI object detection

segnet-tutorial

Files for a tutorial to train SegNet for road scenes using the CamVid dataset

sego

Go中文分词

self-driving-car

The Udacity open source self-driving car project https://blog.csdn.net/a853459004/article/details/53894612

self-driving-car-1

Udacity Self-Driving Car Engineer Nanodegree projects.

semantic3dnet

3D点云分割网络 Point cloud semantic segmentation via Deep 3D Convolutional Neural Network

semantic_slam

CNN (PSPNet) + ORB_SLAM2 语义SLAM Real time semantic slam in ROS with a hand held RGB-D camera

semanticfusion

卷积-反卷积语义分割cnn(基于caffe) + ElasticFusion(稠密SLAM) + CRF融合

senet

Squeeze-and-Excitation Networks 融合局部感受野内的空间信息和通道信息来提取信息特征

sfmlearner

利用CNN学习一个无监督的深度估计和pose估计网络

sgm

CPU & GPU Implementation of SGM(semi-global matching)