Pinned Repositories
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
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
umepon0626's Repositories
umepon0626/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
umepon0626/blog
umepon0626/coding_interview
umepon0626/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
umepon0626/Docker_gpu_env
Docker + GPU + PyTorchの環境
umepon0626/FeedBackPrize
umepon0626/geo
Geospatial primitives and algorithms for Rust
umepon0626/go-stations
umepon0626/infra_practice
AWS練習用リポジトリ
umepon0626/myBlog
umepon0626/next_practice
nextを練習するためのリポジトリ
umepon0626/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。
umepon0626/Rotation-Invariant-Mesh-Difference
RIMD: Efficient and Flexible Deformation Representation for Data-Driven Surface Modeling (Siggraph 2016)
umepon0626/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
umepon0626/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.