Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations

TL;DR

This paper introduces a self-supervised learning method for 3D object recognition that leverages multi-view transformations to learn equivariant representations without labeled data, improving classification and retrieval performance.

Contribution

It proposes a novel self-supervised paradigm, MV-TER, that learns 3D transformation equivariant features from multiple views without requiring labels.

Findings

Outperforms state-of-the-art view-based methods in 3D classification

Demonstrates strong generalization to real-world datasets

Effective in 3D object retrieval tasks

Abstract

3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods is mostly performed in a supervised fashion, which often requires a large amount of data labels with high costs. In contrast, self-supervised learning aims to learn multi-view feature representations without involving labeled data. To this end, we propose a novel self-supervised paradigm to learn Multi-View Transformation Equivariant Representations (MV-TER), exploring the equivariant transformations of a 3D object and its projected multiple views. Specifically, we perform a 3D transformation on a 3D object, and obtain multiple views before and after the transformation via projection. Then, we self-train a representation to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation. Experimental results demonstrate that the proposed MV-TER significantly outperforms the state-of-the-art view-based approaches in 3D object classification and retrieval tasks, and show the generalization to real-world datasets.