  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.
