49 citations · 56 across the 8 of their papers we have counts for
9 papers
DiViNeT: 3D Reconstruction from Disparate Views via Neural Template Regularization
Aditya Vora, Akshay Gadi Patil, Hao Zhang
We present a volume rendering-based neural surface reconstruction method that takes as few as three disparate RGB images as input. Our key idea is to regularize the reconstruction,…
RoSI: Recovering 3D Shape Interiors from Few Articulation Images
Akshay Gadi Patil, Yiming Qian, Shan Yang +3
The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface mesh…
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…
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images
Ruiqi Wang, Akshay Gadi Patil, Fenggen Yu +1
We introduce the first active learning (AL) model for high-accuracy instance segmentation of moveable parts from RGB images of real indoor scenes. Specifically, our goal is to obta…
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…
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction
Jiongchao Jin, Akshay Gadi Patil, Zhang Xiong +1
We introduce a differential visual similarity metric to train deep neural networks for 3D reconstruction, aimed at improving reconstruction quality. The metric compares two 3D shap…