  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.
