Pinned Repositories
30dayMakeOS
《30天自制操作系统》源码中文版。自己制作一个操作系统（OSASK）的过程
3D-Shape-Analysis-Paper-List
A list of recent papers, libraries and datasets about 3D shape/scene analysis (by topics, updating).
AtlasNet
This repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image.
awesome-hand-pose-estimation
Awesome work on hand pose estimation/tracking
batch-xnormal
batch baking tools for game artists
blackhatpython
test code from book blackhatpython
boids
A fast JavaScript implementation of the boids algorithm
boids-1
Boid Flocking Simulation
Generic-Raymarch-Unity
An experiment in distance field raymarching that interacts with standard mesh-based objects. Made in Unity.
Solving-OpenFst-Examples
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HuaMuLanChina's Repositories
HuaMuLanChina/Solving-OpenFst-Examples
玩
HuaMuLanChina/3D-Shape-Analysis-Paper-List
A list of recent papers, libraries and datasets about 3D shape/scene analysis (by topics, updating).
HuaMuLanChina/AtlasNet
This repository contains the source codes for the paper "AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation ". The network is able to synthesize a mesh (point cloud + connectivity) from a low-resolution point cloud, or from an image.
HuaMuLanChina/Bouncer
Unity ML Agents Test
HuaMuLanChina/Brownian
Brownian phenomenon Test
HuaMuLanChina/Cartoon-Smoke-Simple
Cartoon Smoke Simple
HuaMuLanChina/CFD_Julia
This repository contains fundamental codes related to CFD that can be included in any graduate level CFD coursework.
HuaMuLanChina/chord-detection
collection of methods for chord detection (aka polyphonic, multipitch signals) with chromagram outputs and Krumhansl-Schmuckler key estimation
HuaMuLanChina/Cpp17
本书为《C++17 the complete guide》的个人中文翻译，仅供学习和交流使用，侵删
HuaMuLanChina/CppCoreGuidelines
The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
HuaMuLanChina/depthai_hand_tracker
HuaMuLanChina/dgcnn.pytorch
A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
HuaMuLanChina/DGP-Workout-In-Blender
Ref: https://www.bilibili.com/video/BV1B54y1B7Uc
HuaMuLanChina/mediapipe
MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines
HuaMuLanChina/MeshCNN
Convolutional Neural Network for 3D meshes in PyTorch
HuaMuLanChina/muallef
Study of Music Information Retrieval (MIR) methods for multi-pitch estimation and onset detection.
HuaMuLanChina/multi-agent-emergence-environments
Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"
HuaMuLanChina/Music-Transcription-with-Semantic-Segmentation
Automatic music transcription using semantic segmentation model. Reached state-of-the-art score on MAPS and MusicNet.
HuaMuLanChina/numerical-linear-algebra
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
HuaMuLanChina/Piano-Fingering-Generators
Research Project - Piano Fingering Generators
HuaMuLanChina/piano-hands
This dataset contains object tagging of right and left hands on a piano background.
HuaMuLanChina/Picasso
HuaMuLanChina/RPMNet
RPM-Net: Robust Point Matching using Learned Features (CVPR2020)
HuaMuLanChina/rtmp-rtsp-stream-client-java
Library to stream in rtmp and rtsp for Android. All code in Java
HuaMuLanChina/SimpleView
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
HuaMuLanChina/Speech-Separation-Paper-Tutorial
A must-read paper for speech separation based on neural networks
HuaMuLanChina/SubdivNet
Subdivision-based Mesh Convolutional Networks
HuaMuLanChina/VA-GCN
A new GCN model for Point Cloud Analyse
HuaMuLanChina/VCMeshConv
Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.
HuaMuLanChina/Vulkan-Guide
One stop shop for getting started with the Vulkan API