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Neural 3D reconstruction from sparse views using geometric priors

Computational Visual Media 2023年第4期9卷 687-697页

作者： Tai-Jiang Mu Hao-Xiang Chen Jun-Xiong Cai Ning Guo BNRist Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Academy of Military Sciences Beijing 100091China

Sparse view 3D reconstruction has attracted increasing attention with the development of neural implicit 3D *** methods usually only make use of 2D views,requiring a dense set of input views for accurate 3D *** this paper,we show that accurate 3D reconstruction can be achieved by incorporating geometric priors into neural implicit 3D *** method adopts the signed distance function as the 3D representation,and learns a generalizable 3D surface reconstruction model from sparse ***,we build a more effective and sparse feature volume from the input views by using corresponding depth maps,which can be provided by depth sensors or directly predicted from the input *** recover better geometric details by imposing both depth and surface normal constraints in addition to the color loss when training the neural implicit 3D *** demonstrate that our method both outperforms state-of-the-art approaches,and achieves good generalizability.

关键词： sparse views 3D reconstruction volume rendering geometric priors neural implicit 3D representation

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GeoGlue: feature matching with self-supervised geometric priors for high-resolution UAV images

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International Journal of Digital Earth 2023年第1期16卷 1246-1275页

作者： Weijia Bei Xiangtao Fan Hongdeng Jian Xiaoping Du Dongmei Yan Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China

We present GeoGlue,a novel method using high-resolution UAV imagery for accurate feature matching,which is normally challenging due to the complicated *** feature detection methods are performed without guidance of geometric priors(e.g.,geometric lines),lacking enough attention given to salient geometric features which are indispensable for accurate matching due to their stable existence across *** this work,geometric lines arefirstly detected by a CNN-based geometry detector(GD)which is pre-trained in a self-supervised manner through automatically generated ***,geometric lines are naturally vectorized based on GD and thus non-significant features can be disregarded as judged by their disordered geometric morphology.A graph attention network(GAT)is utilized forfinal feature matching,spanning across the image pair with geometric priors informed by *** experiments show that GeoGlue outperforms other state-of-the-art methods in feature-matching accuracy and performance stability,achieving pose estimation with maximum rotation and translation errors under 1%in challenging scenes from benchmark datasets,Tanks&Temples and *** study also proposes thefirst self-supervised deep-learning model for curved line detection,generating geometric priors for matching so that more attention is put on prominent features and improving the visual effect of 3D reconstruction.

关键词： Feature matching geometric priors self-supervised learning graph attention network 3D reconstruction digital earth

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