Neural geometric level of detail: Real-time rendering with implicit 3d shapes

T Takikawa, J Litalien, K Yin, K Kreis… - Proceedings of the …, 2021 - openaccess.thecvf.com
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 …

Abc: A big cad model dataset for geometric deep learning

S Koch, A Matveev, Z Jiang… - Proceedings of the …, 2019 - openaccess.thecvf.com
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 …

Nerfshop: Interactive editing of neural radiance fields

C Jambon, B Kerbl, G Kopanas, S Diolatzis… - Proceedings of the …, 2023 - inria.hal.science
Neural Radiance Fields (NeRFs) have revolutionized novel view synthesis for captured
scenes, with recent methods allowing interactive free-viewpoint navigation and fast training …

[PDF][PDF] Tetrahedral meshing in the wild.

Y Hu, Q Zhou, X Gao, A Jacobson, D Zorin… - ACM Trans …, 2018 - slides.games-cn.org
Tetrahedral Meshing in the Wild Page 1 Tetrahedral Meshing in the Wild Yixin Hu , Qingnan
Zhou , **s
M Rabinovich, R Poranne, D Panozzo… - ACM Transactions on …, 2017 - dl.acm.org
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 …

Computational inverse design of surface-based inflatables

J Panetta, F Isvoranu, T Chen, E Siéfert… - ACM Transactions on …, 2021 - dl.acm.org
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 …