33 citations · 74 across the 17 of their papers we have counts for
4 papers · 1 filter
UV-Net: Learning from Boundary Representations
Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne +4
We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep forma…
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning
Aditya Sanghi
A major endeavor of computer vision is to represent, understand and extract structure from 3D data. Towards this goal, unsupervised learning is a powerful and necessary tool. Most…
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb +3
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks. There is a growing…
How Powerful Are Randomly Initialized Pointcloud Set Functions?
Aditya Sanghi, Pradeep Kumar Jayaraman
We study random embeddings produced by untrained neural set functions, and show that they are powerful representations which well capture the input features for downstream tasks su…