Text-guided Synthetic Geometric Augmentation for Zero-shot 3D Understanding

• URL: http://arxiv.org/abs/2501.09278v2
• Date: Fri, 17 Jan 2025 08:20:59 GMT
• Title: Text-guided Synthetic Geometric Augmentation for Zero-shot 3D Understanding
• Authors: Kohei Torimi, Ryosuke Yamada, Daichi Otsuka, Kensho Hara, Yuki M. Asano, Hirokatsu Kataoka, Yoshimitsu Aoki,
• Abstract summary: Textguided Geometric Augmentation (TeGA) is tailored for language-image-3D pretraining, which achieves SoTA in zero-shot 3D classification. We show that TeGA effectively bridges the 3D data gap, enabling robust zero-shot 3D classification even with limited real training data.
• Score: 27.755532663325244
• License:
• Abstract: Zero-shot recognition models require extensive training data for generalization. However, in zero-shot 3D classification, collecting 3D data and captions is costly and laborintensive, posing a significant barrier compared to 2D vision. Recent advances in generative models have achieved unprecedented realism in synthetic data production, and recent research shows the potential for using generated data as training data. Here, naturally raising the question: Can synthetic 3D data generated by generative models be used as expanding limited 3D datasets? In response, we present a synthetic 3D dataset expansion method, Textguided Geometric Augmentation (TeGA). TeGA is tailored for language-image-3D pretraining, which achieves SoTA in zero-shot 3D classification, and uses a generative textto-3D model to enhance and extend limited 3D datasets. Specifically, we automatically generate text-guided synthetic 3D data and introduce a consistency filtering strategy to discard noisy samples where semantics and geometric shapes do not match with text. In the experiment to double the original dataset size using TeGA, our approach demonstrates improvements over the baselines, achieving zeroshot performance gains of 3.0% on Objaverse-LVIS, 4.6% on ScanObjectNN, and 8.7% on ModelNet40. These results demonstrate that TeGA effectively bridges the 3D data gap, enabling robust zero-shot 3D classification even with limited real training data and paving the way for zero-shot 3D vision application.

Related papers

• Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation [66.75243908044538]
  We introduce Zero-1-to-G, a novel approach to direct 3D generation on Gaussian splats using pretrained 2D diffusion models. To incorporate 3D awareness, we introduce cross-view and cross-attribute attention layers, which capture complex correlations and enforce 3D consistency across generated splats. This makes Zero-1-to-G the first direct image-to-3D generative model to effectively utilize pretrained 2D diffusion priors, enabling efficient training and improved generalization to unseen objects.
  arXiv  Detail & Related papers  (2025-01-09T18:37:35Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data [50.164670363633704]
  We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets from text prompts. Our model is directly trained on extensive noisy and unaligned in-the-wild' 3D assets. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation.
  arXiv  Detail & Related papers  (2024-06-06T17:58:15Z)
• Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors [16.93758384693786]
  Bidirectional Diffusion(BiDiff) is a unified framework that incorporates both a 3D and a 2D diffusion process. Our model achieves high-quality, diverse, and scalable 3D generation.
  arXiv  Detail & Related papers  (2023-12-07T10:00:04Z)
• TPA3D: Triplane Attention for Fast Text-to-3D Generation [28.33270078863519]
  We propose Triplane Attention for text-guided 3D generation (TPA3D) TPA3D is an end-to-end trainable GAN-based deep learning model for fast text-to-3D generation. We show that TPA3D generates high-quality 3D textured shapes aligned with fine-grained descriptions.
  arXiv  Detail & Related papers  (2023-12-05T10:39:37Z)
• Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field [16.15190186574068]
  We propose Lift3D, an inverted 2D-to-3D generation framework to achieve the data generation objectives. By lifting well-disentangled 2D GAN to 3D object NeRF, Lift3D provides explicit 3D information of generated objects. We evaluate the effectiveness of our framework by augmenting autonomous driving datasets.
  arXiv  Detail & Related papers  (2023-04-07T07:43:02Z)
• DreamFusion: Text-to-3D using 2D Diffusion [52.52529213936283]
  Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. Our approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.
  arXiv  Detail & Related papers  (2022-09-29T17:50:40Z)
• RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection [138.2892824662943]
  A promising solution is to make better use of the synthetic dataset, which consists of CAD object models, to boost the learning on real datasets. Recent work on 3D pre-training exhibits failure when transfer features learned on synthetic objects to other real-world applications. In this work, we put forward a new method called RandomRooms to accomplish this objective.
  arXiv  Detail & Related papers  (2021-08-17T17:56:12Z)
• Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation [107.07047303858664]
  Large-scale human datasets with 3D ground-truth annotations are difficult to obtain in the wild. We address this problem by augmenting existing 2D datasets with high-quality 3D pose fits. The resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks.
  arXiv  Detail & Related papers  (2020-04-07T20:21:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.