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.
@article{arxiv.2206.12356,
title = {HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction},
author = {Zhenpei Yang and Zaiwei Zhang and Qixing Huang},
journal= {arXiv preprint arXiv:2206.12356},
year = {2022}
}