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interview

📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, etc.

intrinsic_flow

Pytorch implementation of the CVPR 2019 paper Dense Intrinsic Appearance Flow for Human Pose Transfer.

kgraph

A library for k-nearest neighbor search

kl-loss

Bounding Box Regression with Uncertainty for Accurate Object Detection (CVPR'19)

maskfusion

MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects

maskscoring_rcnn

Codes for paper "Mask Scoring R-CNN".

menpobenchmark

Multi-pose 2D and 3D Face Alignment & Tracking

msgan

MSGAN: Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis (CVPR2019)

mvfnet

Pytorch code for paper: MVF-Net: Multi-View 3D Face Morphable Model Regression

ncmdump

netease cloud music copyright protection file dump

neural_style_learn

learn neural style

ochumanapi

API for the dataset proposed in "Pose2Seg: Detection Free Human Instance Segmentation" @ CVPR2019.

pvnet

Code for "PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation" CVPR 2019 oral

python

All Algorithms implemented in Python

sfsnet

shapefromtexture

Implementation of paper: Toward a Universal Model for Shape from Texture

sparseicp

Automatically exported from code.google.com/p/sparseicp

spatial-transformer-network

A Tensorflow implementation of Spatial Transformer Networks.

staticfusion

StaticFusion

taichi

Productive programming language for portable, high-performance, sparse & differentiable computing

tensorflow-eager-execution-examples

TensorFlow Eager Execution Tutorial and Examples for Beginners

tf_mesh_renderer

A differentiable, 3D mesh renderer using TensorFlow.

tsdf-fusion

Fuse multiple depth frames into a TSDF voxel volume.

unet-gan-matting

Background Removal based on U-Net

wetahair

Implementation of "Importance Sampling for Physically-Based Hair Fiber Models"