49 citations · 59 across the 6 of their papers we have counts for
6 papers
Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes
Akshay Gadi Patil, Supriya Gadi Patil, Manyi Li +3
This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We des…
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…
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…
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…
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
Akshay Gadi Patil, Manyi Li, Matthew Fisher +2
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout m…
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
Manyi Li, Akshay Gadi Patil, Kai Xu +7
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently. Our key observation…