OARecon: Object-Aware Viewpoint Augmentation for Indoor Compositional Reconstruction

Abstract

Real-world scenes likely involve repetitive objects indicating that the reconstruction of the target object can be supplemented by the views of other identical objects. However, traditional 3D reconstruction methods do not take this a priori knowledge into account and fail to make full use of the available information. In this paper, we propose an object-aware viewpoint augmentation scheme for indoor compositional reconstruction. Within this scheme, a viewpoint supplementation strategy based on signed distance function and neural radiance fields is proposed to fully leverage the information from repetitive objects such that the occlusion problem is suppressed. Moreover, this scheme introduces monocular uncertainty priors and regional smoothness constraints to enhance the reconstruction accuracy of slender and thin structures and the smoothness of occluded background, respectively. Experimental results considering both synthetic and real-world scenes demonstrate that our method effectively improves the reconstruction quality of repetitive objects and background.