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.

翻译：近年来，基于深度学习的3D模型研究在生成、检测和场景理解领域取得了突破性进展。然而，这类算法的有效性在很大程度上依赖于大规模训练数据集。针对这一挑战，我们提出了一种名为高效3D接缝裁剪（E3SC）的新型3D模型增强方法。该方法基于接缝裁剪技术，仅对输入模型的部分区域进行渐进式变形，同时确保整体语义保持不变。实验表明，我们的方法能够针对不同类别和风格的输入模型生成多样化且高质量的增强3D形状，其性能显著优于现有方法。定量评估进一步证明，该方法能有效提升后续3D生成算法所产生形状的新颖性与质量。