78 citations · 145 across the 5 of their papers we have counts for
11 papers
Learning Mesh Representations via Binary Space Partitioning Tree Networks
Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang
Polygonal meshes are ubiquitous, but have only played a relatively minor role in the deep learning revolution. State-of-the-art neural generative models for 3D shapes learn implici…
Neural Marching Cubes
Zhiqin Chen, Hao Zhang
We introduce Neural Marching Cubes (NMC), a data-driven approach for extracting a triangle mesh from a discretized implicit field. Classical MC is defined by coarse tessellation te…
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
Fenggen Yu, Zhiqin Chen, Manyi Li +4
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive…
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
Jiongchao Jin, Arezou Fatemi, Wallace Lira +5
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research. The new dataset contains the largest number of rainy images (58,…
DIM-Net: Learning Detail Disentangled Implicit Fields from Single Images
Manyi Li, Hao Zhang
We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface f…
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
Zihao Yan, Ruizhen Hu, Xingguang Yan +4
We introduce RPM-Net, a deep learning-based approach which simultaneously infers movable parts and hallucinates their motions from a single, un-segmented, and possibly partial, 3D…