94 citations · 431 across the 15 of their papers we have counts for
12 papers · 1 filter
SiZeUp: Fast 3D Proxy from Aerial Images via Depth Ordinal Loss
Wenjun Zhou, Yunshan Li, Qiaoyu Zhu +4
We present SiZeUp, a fast and scalable approach for constructing large-scale 3D urban proxy models directly from calibrated oblique aerial imagery. Our method adopts a height-from-…
Neural Dual Contouring
Zhiqin Chen, Andrea Tagliasacchi, Thomas Funkhouser +1
We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per…
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures
Chengjie Niu, Manyi Li, Kai Xu +1
We introduce RIM-Net, a neural network which learns recursive implicit fields for unsupervised inference of hierarchical shape structures. Our network recursively decomposes an inp…
UNIST: Unpaired Neural Implicit Shape Translation Network
Qimin Chen, Johannes Merz, Aditya Sanghi +3
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding imp…
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…
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…
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher +2
We treat shape co-segmentation as a representation learning problem and introduce BAE-NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained with a…
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Kangxue Yin, Zhiqin Chen, Hui Huang +2
We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and c…
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
Zili Yi, Zhiqin Chen, Hao Cai +3
We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations…