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Deep panoramic depth prediction and completion for indoor scenes

Computational Visual Media 2024年第5期10卷 903-922页

作者： giovanni pintore Eva Almansa Armando Sanchez Giorgio Vassena Enrico Gobbetti Visual and Data-intensive Computing CRS4Cagliari 09134Italy Gexcel srl Elmas(CA)09097Italy Department of Civil EnvironmentArchitectural Engineeringand Mathematics(DICATAM)Universita degli Studi di Brescia(UNIBS)Brescia(BS)25123Italy.

We introduce a novel end-to-end deeplearning solution for rapidly estimating a dense spherical depth map of an indoor *** input is a single equirectangular image registered with a sparse depth map,as provided by a variety of common capture *** is inferred by an efficient and lightweight single-branch network,which employs a dynamic gating system to process together dense visual data and sparse geometric *** exploit the characteristics of typical man-made environments to efficiently compress multiresolution features and find short-and long-range relations among scene ***,we introduce a new augmentation strategy to make the model robust to different types of sparsity,including those generated by various structured light sensors and LiDAR *** experimental results demonstrate that our method provides interactive performance and outperforms stateof-the-art solutions in computational efficiency,adaptivity to variable depth sparsity patterns,and prediction accuracy for challenging indoor data,even when trained solely on synthetic data without any fine tuning.

关键词： machine learning image processing and computervision visionand scene understanding 3D stereo scene analysis

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3D floor plan recovery from overlapping spherical images

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Computational Visual Media 2018年第4期4卷 367-383页

作者： giovanni pintore Fabio Ganovelli Ruggero Pintus Roberto Scopigno Enrico Gobbetti CRS4 Visual Computing Group CNR-ISTI Visual Computing Group

We present a novel approach to automatically recover, from a small set of partially overlapping spherical images, an indoor structure representation in terms of a 3D floor plan registered with a set of 3D environment maps. We introduce several improvements over previous approaches based on color and spatial reasoning exploiting Manhattan world priors. In particular, we introduce a new method for geometric context extraction based on a 3D facet representation,which combines color distribution analysis of individual images with sparse multi-view clues. We also introduce an efficient method to combine the facets from different viewpoints in a single consistent model, taking into the reliability of the facet information. The resulting capture and reconstruction pipeline automatically generates 3D multi-room environments in cases where most previous approaches fail, e.g., in the presence of hidden corners and large clutter, without the need for additional dense 3D data or tools. We demonstrate the effectiveness and performance of our approach on different real-world indoor scenes. Our test data is available to allow further studies and comparisons.

关键词： indoor reconstruction spherical panoramic cameras 360 degree photography multi-room environments

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