Pairwise shape studies in 3D deep learning
Details
Recently, deep learning has achieved impressive success on modeling and understanding 3D shapes. It becomes a fundamental research question how the learning based methods are generalizable to a collection of shapes in various geometry.
This talk will discuss two scenarios where studying the interpolation between a pair of shapes helps to improve and understanding the generalization of 3D deep learning models. We show that interpolation on the raw geometry of two-point clouds helps to improve the performance of point cloud classification (Part 1), while hidden feature-level interpolation helps to understand how the latent-conditioned implicit neural representations generalize to representing different 3D shapes (Part 2).
Lecture slides: https://drive.google.com/file/d/1DIR4MDOMdS0O8GR5CxxYC_3mgfFaabie/view?usp=sharing
This talk is based on the following papers by the speaker:
-
PointMixup: Augmentation for Point Clouds.
ECCV 2020
https://arxiv.org/abs/2008.06374
https://github.com/yunlu-chen/PointMixup -
Neural Feature Matching in Implicit 3D Representations.
ICML 2021
http://proceedings.mlr.press/v139/chen21f/chen21f.pdf
Presenter BIO:
Yunlu Chen is a PhD candidate at University of Amsterdam, advised by Dr Efstratios Gavves. His research focuses on 3D deep learning, including monocular depth estimation, RGB-D semantic segmentation, point cloud understanding and implicit neural representations.
His git: https://github.com/yunlu-chen
-------------------------
Find us at:
All lectures are uploaded to our Youtube channel ➜ https://www.youtube.com/channel/UCHObHaxTXKFyI_EI8HiQ5xw
Newsletter for updates about more events ➜ http://eepurl.com/gJ1t-D
Sub-reddit for discussions ➜ https://www.reddit.com/r/2D3DAI/
Discord server for, well, discord ➜ https://discord.gg/MZuWSjF
Blog ➜ https://2d3d.ai
AI Consultancy -> https://abelians.com