# Learning 3D Object Categories by Looking Around Them

## TL;DR

This paper introduces a novel method for learning 3D object categories from videos without manual labels, utilizing a Siamese network for viewpoint alignment and a shape completion network, achieving state-of-the-art results.

## Contribution

It presents a new approach combining viewpoint alignment and shape completion networks that learn 3D categories from unannotated videos, avoiding manual supervision.

## Key findings

- Achieved state-of-the-art results on benchmark datasets.
- Demonstrated the effectiveness of probabilistic predictions.
- Showed benefits of geometry-aware data augmentation.

## Abstract

Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.