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Neural geometric level of detail: Real-time rendering with implicit 3d shapes
Neural signed distance functions (SDFs) are emerging as an effective representation for 3D
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …
Abc: A big cad model dataset for geometric deep learning
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD)
models for research of geometric deep learning methods and applications. Each model is a …
models for research of geometric deep learning methods and applications. Each model is a …
Nerfshop: Interactive editing of neural radiance fields
Neural Radiance Fields (NeRFs) have revolutionized novel view synthesis for captured
scenes, with recent methods allowing interactive free-viewpoint navigation and fast training …
scenes, with recent methods allowing interactive free-viewpoint navigation and fast training …
[PDF][PDF] Tetrahedral meshing in the wild.
Tetrahedral Meshing in the Wild Page 1 Tetrahedral Meshing in the Wild Yixin Hu , Qingnan
Zhou , **s
Zhou , **s
We present a scalable approach for the optimization of flip-preventing energies in the
general context of simplicial map**s and specifically for mesh parameterization. Our …
general context of simplicial map**s and specifically for mesh parameterization. Our …
Computational inverse design of surface-based inflatables
We present a computational inverse design method for a new class of surface-based
inflatable structure. Our deployable structures are fabricated by fusing together two layers of …
inflatable structure. Our deployable structures are fabricated by fusing together two layers of …