论文标题
HM3D-ABO：一个以对象为中心的多视图3D重建的照片真实数据集
HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
论文作者
论文摘要
重建3D对象是重要的计算机视觉任务，在AR/VR中具有广泛的应用。为此任务开发的深度学习算法通常依赖于不切实际的合成数据集，例如shapenet和things3d。另一方面，现有的以对象为中心的数据集通常没有足够的注释来实现监督培训或可靠的评估。在此技术报告中，我们提出了一个以照片为中心的对象数据集HM3D-ABO。它是通过构成现实的室内场景和现实对象来构建的。对于每种配置，我们提供多视图RGB观测值，这是对象，地面真实深度图和对象掩码的水密网格模型。所提出的数据集也可用于诸如摄像头估计和新颖视图合成之类的任务。数据集生成代码在https://github.com/zhenpeiyang/hm3d-abo上发布。
Reconstructing 3D objects is an important computer vision task that has wide application in AR/VR. Deep learning algorithm developed for this task usually relies on an unrealistic synthetic dataset, such as ShapeNet and Things3D. On the other hand, existing real-captured object-centric datasets usually do not have enough annotation to enable supervised training or reliable evaluation. In this technical report, we present a photo-realistic object-centric dataset HM3D-ABO. It is constructed by composing realistic indoor scene and realistic object. For each configuration, we provide multi-view RGB observations, a water-tight mesh model for the object, ground truth depth map and object mask. The proposed dataset could also be useful for tasks such as camera pose estimation and novel-view synthesis. The dataset generation code is released at https://github.com/zhenpeiyang/HM3D-ABO.
