论文标题
3D未来：3D家具形状与质地
3D-FUTURE: 3D Furniture shape with TextURE
论文作者
论文摘要
当前3D基准中的3D CAD形状主要是从在线模型存储库中收集的。因此，它们通常没有足够的几何细节和信息质量不足，因此在高质量的3D网格和纹理恢复等领域的全面和微妙研究中，它们的吸引力降低了。本文呈现3D家具形状的质地（3D-Future）：在家庭场景中，富裕的3D家具形状的大规模存储库。在这份技术报告时，3D未来包含20,240个5,000间不同房间的清洁合成图像。有9,992个具有高分辨率纹理的家具的独特详细3D实例。经验丰富的设计师开发了房间场景，现场的3D CAD形状用于工业生产。鉴于组织良好的3D未来，我们在几个广泛研究的任务上提供了基线实验，例如联合2D实例分割和3D对象姿势估计，基于图像的3D形状检索，单个图像中的3D对象重建以及3D形状的纹理恢复，以促进我们数据库的未来相关研究。
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textures, making them less attractive for comprehensive and subtle research in areas such as high-quality 3D mesh and texture recovery. This paper presents 3D Furniture shape with TextURE (3D-FUTURE): a richly-annotated and large-scale repository of 3D furniture shapes in the household scenario. At the time of this technical report, 3D-FUTURE contains 20,240 clean and realistic synthetic images of 5,000 different rooms. There are 9,992 unique detailed 3D instances of furniture with high-resolution textures. Experienced designers developed the room scenes, and the 3D CAD shapes in the scene are used for industrial production. Given the well-organized 3D-FUTURE, we provide baseline experiments on several widely studied tasks, such as joint 2D instance segmentation and 3D object pose estimation, image-based 3D shape retrieval, 3D object reconstruction from a single image, and texture recovery for 3D shapes, to facilitate related future researches on our database.
