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blog

coding_interview

dev_environment

開発環境やツールなどの設定ファイルを管理する用

docker_gpu_env

Docker + GPU + PyTorchの環境

feedbackprize

geo

Geospatial primitives and algorithms for Rust

go-stations

infra_practice

AWS練習用リポジトリ

myblog

next_practice

nextを練習するためのリポジトリ

pyside_practice

pysideを使ってGUIアプリケーションを作成します。練習です。

rotation-invariant-mesh-difference

RIMD: Efficient and Flexible Deformation Representation for Data-Driven Surface Modeling (Siggraph 2016)

tech_blog

🚀 Astro boilerplate with responsive blog and portfolio template using TypeScript and React styled with Tailwind CSS ⚡️ Made with developer experience first: TypeScript + ESLint + Prettier + Husky + Lint-Staged + Commitlint + VSCode

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.

yumeshop-frontend

ゆめみフロントエンドインターン課題の開発環境リポジトリ