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3D Shape Augmentation with Content-Aware Shape Resizing (2405.09050v1)
Published 15 May 2024 in cs.CV
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.
- Saliency detection for content-aware image resizing. In 2009 16th IEEE international conference on image processing (ICIP), pages 1005–1008. IEEE, 2009.
- A comprehensive review on content-aware image retargeting: From classical to state-of-the-art methods. Signal Processing, 195:108496, 2022.
- Seam carving for content-aware image resizing. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pages 609–617. 2023.
- Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
- Augnet: End-to-end unsupervised visual representation learning with image augmentation. arXiv preprint arXiv:2106.06250, 2021.
- Parsing line segments of floor plan images using graph neural networks. arXiv preprint arXiv:2303.03851, 2023.
- Optimized image resizing using seam carving and scaling. ACM Transactions on Graphics (TOG), 28(5):1–10, 2009.
- Stretchability-aware block scaling for image retargeting. Journal of visual communication and image representation, 24(4):499–508, 2013.
- Adaptive data augmentation for image classification. In 2016 IEEE international conference on image processing (ICIP), pages 3688–3692. Ieee, 2016.
- Gan-based data augmentation for improved liver lesion classification. 2018.
- Feature-aware texturing. Rendering Techniques, 2006(17th):2, 2006.
- Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015.
- Associative deep clustering: Training a classification network with no labels. In Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, Proceedings 40, pages 18–32. Springer, 2019.
- Content-aware image resizing: An improved and shadow-preserving seam carving method. Signal Processing, 155:233–246, 2019.
- Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729–9738, 2020.
- Neural wavelet-domain diffusion for 3d shape generation. In SIGGRAPH Asia 2022 Conference Papers, pages 1–9, 2022.
- Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9865–9874, 2019.
- Patchwise scaling method for content-aware image resizing. Signal Processing, 92(5):1243–1257, 2012.
- Deepir: A deep semantics driven framework for image retargeting. In 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pages 54–59. IEEE, 2019.
- A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88, 2017.
- Composing semantic collage for image retargeting. IEEE Transactions on Image Processing, 27(10):5032–5043, 2018.
- Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440, 2015.
- Deep learning for understanding satellite imagery: An experimental survey. Frontiers in Artificial Intelligence, 3:534696, 2020.
- Polygen: An autoregressive generative model of 3d meshes. In International conference on machine learning, pages 7220–7229. PMLR, 2020.
- You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016.
- Texture: Text-guided texturing of 3d shapes. arXiv preprint arXiv:2302.01721, 2023.
- Improved seam carving for video retargeting. ACM transactions on graphics (TOG), 27(3):1–9, 2008.
- Multi-operator media retargeting. ACM Transactions on graphics (TOG), 28(3):1–11, 2009.
- David Sculley. Web-scale k-means clustering. In Proceedings of the 19th international conference on World wide web, pages 1177–1178, 2010.
- A survey on image data augmentation for deep learning. Journal of big data, 6(1):1–48, 2019.
- Meshgpt: Generating triangle meshes with decoder-only transformers. arXiv preprint arXiv:2311.15475, 2023.
- Carvingnet: content-guided seam carving using deep convolution neural network. IEEE Access, 7:284–292, 2018.
- A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognition, page 109347, 2023.
- Style-consistent image translation: A novel data augmentation paradigm to improve plant disease recognition. Frontiers in Plant Science, 12:3361, 2022.
- Pointflow: 3d point cloud generation with continuous normalizing flows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4541–4550, 2019.
- Generative adversarial network in medical imaging: A review. Medical image analysis, 58:101552, 2019.
- Locally attentional sdf diffusion for controllable 3d shape generation. arXiv preprint arXiv:2305.04461, 2023.
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