3D Shape Augmentation with Content-Aware Shape Resizing

TL;DR

This paper introduces E3SC, a novel 3D shape augmentation technique based on seam carving, which deforms models while preserving semantics, thereby improving the diversity and quality of generated 3D shapes for deep learning.

Contribution

The paper presents a new 3D augmentation method using content-aware shape resizing that enhances data diversity without altering model semantics.

Findings

Produces diverse, high-quality augmented 3D shapes

Improves the novelty and quality of shapes generated by other algorithms

Achieves significant improvements over previous augmentation methods

Abstract

Recent advancements in deep learning for 3D models have propelled breakthroughs in generation, detection, and scene understanding. However, the effectiveness of these algorithms hinges on large training datasets. We address the challenge by introducing Efficient 3D Seam Carving (E3SC), a novel 3D model augmentation method based on seam carving, which progressively deforms only part of the input model while ensuring the overall semantics are unchanged. Experiments show that our approach is capable of producing diverse and high-quality augmented 3D shapes across various types and styles of input models, achieving considerable improvements over previous methods. Quantitative evaluations demonstrate that our method effectively enhances the novelty and quality of shapes generated by other subsequent 3D generation algorithms.