面向真实 3D 物体的感知、理解、重建与生成是计算机视觉领域一直倍受关注的问题，也在近年来取得了飞速的进展。然而，由于社区中长期缺乏大规模的实采 3D 物体数据库，大部分技术方法仍依赖于 ShapeNet[1] 等仿真数据集。再者，仿真数据与真实数据之间的外观和分布差距巨大，这大大限制了它们在现实生活中的应用。为了解决这一困难，近年来也有一些优秀的工作如 CO3D[2] 等从视频/多视角图片中寻求突破点，并利用 SfM 的方式重建 3D 点云，然而这种方式得到的点云往往难以提供完整、干净、精准的 3D 表面和纹理。因此，社区迫切需要一个大规模且高质量的真实世界 3D 物体扫描数据集，这将有助于推进许多3D视觉任务和下游应用。
关于数据集本身，我们会致力于不断扩大和更新数据集以满足更广泛的研究需求。除了现有的应用，我们还计划进一步发展其他下游任务，如 2D / 3D 物体检测和 6D 姿态估计等。除了感知和重建任务外，在 AIGC 时代，我们相信 OmniObject3D 能够在推动真实感 3D 生成方面发挥至关重要的作用。
References：
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