Pairwise shape studies in 3D deep learning

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:

1. PointMixup: Augmentation for Point Clouds.
   ECCV 2020
   https://arxiv.org/abs/2008.06374
   https://github.com/yunlu-chen/PointMixup

2. 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

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