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Dora-Bench|3D形状重建数据集|几何细节评估数据集
收藏arXiv2024-12-24 更新2024-12-26 收录
下载链接：
https://aruichen.github.io/Dora
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Dora-Bench是一个用于评估3D形状变分自编码器（VAE）重建质量的基准测试数据集，由香港科技大学、字节跳动Seed、LightIllusions和清华大学联合开发。该数据集包含来自多个公开数据集的3D形状，并根据几何复杂度将其分为四个等级：较少细节、中等细节、丰富细节和非常丰富细节。Dora-Bench通过引入Sharp Normal Error (SNE)度量标准，专注于评估几何细节的重建精度，从而提供了比传统随机采样方法更严格的评估框架。该数据集旨在解决3D形状重建中的几何细节丢失问题，推动3D内容生成领域的研究。
提供机构：
香港科技大学, 字节跳动Seed, LightIllusions, 清华大学
创建时间：
2024-12-24
原始信息汇总
Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
数据集概述
Dora 是一个用于3D形状变分自编码器（Variational Auto-Encoders, VAEs）的采样和基准测试的数据集。该数据集旨在为3D形状生成和重建任务提供标准化的评估框架。
作者信息
- Rui Chen<sup>1,2</sup>
- Jianfeng Zhang<sup>2*</sup>
- Yixun Liang<sup>1,3</sup>
- Guan Luo<sup>2,4</sup>
- Weiyu Li<sup>1,3</sup>
- Jiarui Liu<sup>1,3</sup>
- Xiu Li<sup>2</sup>
- Xiaoxiao Long<sup>1,3</sup>
- Jiashi Feng<sup>2</sup>
- Ping Tan<sup>1,3*</sup>
通讯作者
- Jianfeng Zhang 和 Ping Tan 为通讯作者。
所属机构
- 香港科技大学（The Hong Kong University of Science and Technology）
- 字节跳动种子（Bytedance Seed）
- LightIllusions
- 清华大学（Tsinghua University）
相关资源
AI搜集汇总
数据集介绍
构建方式
Dora-Bench数据集的构建旨在系统评估3D变分自编码器（VAE）在不同几何复杂度下的重建质量。该数据集从多个公开数据集中精选了3D模型，包括ABO、GSO、Meta和Objaverse，并根据几何复杂度将模型分为四个等级。每个等级包含约800个样本，确保数据集的多样性和代表性。为了量化几何复杂度，Dora-Bench引入了锐边数量作为分类标准，并通过锐边采样策略（SES）确保模型在训练过程中能够优先捕捉几何细节。此外，数据集还引入了锐法线误差（SNE）这一新指标，专门用于评估模型在几何显著区域的重建精度。
特点
Dora-Bench数据集的主要特点在于其系统化的几何复杂度分类和针对几何细节的评估指标。数据集将3D模型分为四个复杂度等级，从低细节到高细节，覆盖了广泛的几何特征。通过锐边采样策略，数据集能够有效捕捉模型中的几何显著区域，从而提升重建质量。此外，锐法线误差（SNE）指标的引入使得评估更加精细化，能够准确衡量模型在几何细节上的表现。Dora-Bench不仅提供了丰富的测试样本，还通过多视角渲染和Canny边缘检测等技术，确保评估过程的严谨性和全面性。
使用方法
Dora-Bench数据集的使用方法主要包括模型训练和评估两个阶段。在训练阶段，研究人员可以利用数据集中的3D模型，结合锐边采样策略（SES）和双交叉注意力机制（DCA），训练3D变分自编码器（VAE）。在评估阶段，数据集提供了多个评估指标，包括F-score、Chamfer距离和锐法线误差（SNE），用于全面衡量模型的重建质量。研究人员可以通过对比不同复杂度等级下的模型表现，深入分析模型在几何细节捕捉上的能力。此外，Dora-Bench还可用于下游任务，如单图像到3D生成，通过结合扩散模型进一步提升生成质量。
背景与挑战
背景概述
Dora-Bench数据集由香港科技大学、字节跳动Seed、LightIllusions和清华大学的研究团队于2024年提出，旨在解决3D形状变分自编码器（VAE）在形状重建中的几何细节丢失问题。该数据集的核心研究问题是通过引入锐边采样策略和双交叉注意力机制，提升VAE在复杂几何特征上的重建精度。Dora-Bench的提出为3D形状建模领域提供了系统化的评估基准，推动了3D内容生成技术的发展，尤其在游戏、电影和AR/VR等领域的应用中具有重要意义。
当前挑战
Dora-Bench面临的挑战主要包括两个方面：首先，在领域问题方面，传统的均匀采样策略难以捕捉3D形状中的高复杂度几何细节，导致重建质量下降，尤其是在复杂形状的边缘和细节区域。其次，在数据集构建过程中，如何设计一种能够有效识别和优先采样几何显著区域的算法，同时保持全局结构的完整性，是一个技术难点。此外，开发一种能够量化形状复杂度并评估重建精度的新指标（如锐边法向误差）也面临挑战，需要确保其在不同复杂度形状上的普适性和准确性。
常用场景
经典使用场景
Dora-Bench数据集在3D形状生成和重建领域具有广泛的应用，特别是在基于变分自编码器（VAE）的3D形状建模中。该数据集通过引入锐边采样策略和双交叉注意力机制，显著提升了3D形状重建的精度，尤其是在捕捉复杂几何细节方面。Dora-Bench的经典使用场景包括3D形状的压缩、重建以及生成任务，尤其是在需要高保真几何细节的领域，如游戏、电影和增强现实/虚拟现实（AR/VR）中。
实际应用
Dora-Bench数据集在实际应用中具有广泛的价值，特别是在3D内容生成领域。通过其高效的锐边采样策略和双交叉注意力机制，该数据集能够生成高质量的3D形状，适用于游戏、电影、AR/VR等领域的3D建模任务。此外，Dora-Bench还为单图像到3D生成任务提供了强大的支持，通过其紧凑的潜在表示，显著提升了生成模型的效率和生成质量。这些应用使得Dora-Bench成为3D内容生成领域的重要工具。
衍生相关工作
Dora-Bench数据集的推出催生了一系列相关研究工作，特别是在3D形状生成和重建领域。基于Dora-Bench的锐边采样策略和双交叉注意力机制，许多研究团队进一步优化了3D VAE的设计，提升了重建和生成的质量。此外，Dora-Bench还推动了3D形状生成模型的评估方法的发展，特别是通过其引入的锐边法向误差（SNE）指标，使得对几何细节的评估更加精确。这些衍生工作不仅扩展了Dora-Bench的应用范围，还推动了3D内容生成技术的进步。
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