UniG3D: A Unified 3D Object Generation Dataset

• URL: http://arxiv.org/abs/2306.10730v1
• Date: Mon, 19 Jun 2023 07:03:45 GMT
• Title: UniG3D: A Unified 3D Object Generation Dataset
• Authors: Qinghong Sun, Yangguang Li, ZeXiang Liu, Xiaoshui Huang, Fenggang Liu, Xihui Liu, Wanli Ouyang, Jing Shao
• Abstract summary: UniG3D is a unified 3D object generation dataset constructed by employing a universal data transformation pipeline on ShapeNet datasets. This pipeline converts each raw 3D model into comprehensive multi-modal data representation. The selection of data sources for our dataset is based on their scale and quality.
• Score: 75.49544172927749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of generative AI has a transformative impact on various areas, including virtual reality, autonomous driving, the metaverse, gaming, and robotics. Among these applications, 3D object generation techniques are of utmost importance. This technique has unlocked fresh avenues in the realm of creating, customizing, and exploring 3D objects. However, the quality and diversity of existing 3D object generation methods are constrained by the inadequacies of existing 3D object datasets, including issues related to text quality, the incompleteness of multi-modal data representation encompassing 2D rendered images and 3D assets, as well as the size of the dataset. In order to resolve these issues, we present UniG3D, a unified 3D object generation dataset constructed by employing a universal data transformation pipeline on Objaverse and ShapeNet datasets. This pipeline converts each raw 3D model into comprehensive multi-modal data representation <text, image, point cloud, mesh> by employing rendering engines and multi-modal models. These modules ensure the richness of textual information and the comprehensiveness of data representation. Remarkably, the universality of our pipeline refers to its ability to be applied to any 3D dataset, as it only requires raw 3D data. The selection of data sources for our dataset is based on their scale and quality. Subsequently, we assess the effectiveness of our dataset by employing Point-E and SDFusion, two widely recognized methods for object generation, tailored to the prevalent 3D representations of point clouds and signed distance functions. Our dataset is available at: https://unig3d.github.io.

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