HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction

• URL: http://arxiv.org/abs/2206.12356v1
• Date: Fri, 24 Jun 2022 16:02:01 GMT
• Title: HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
• Authors: Zhenpei Yang, Zaiwei Zhang, Qixing Huang
• Abstract summary: We present a photo-realistic object-centric dataset HM3D-ABO. It is constructed by composing realistic indoor scene and realistic object. The dataset could also be useful for tasks such as camera pose estimation and novel-view synthesis.
• Score: 37.29140654256627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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