Dense 3D point cloud reconstruction using a deep pyramid network

Mandikal, P and Babu, RV (2019) Dense 3D point cloud reconstruction using a deep pyramid network. In: UNSPECIFIED, pp. 1052-1060.

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Abstract

Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs. © 2019 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	Copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	3D modeling; Computer architecture; Computer vision; Image reconstruction; Network architecture, 3D point cloud; High resolution; High-resolution output; Pyramid network; Qualitative evaluations; Scalable architectures; State of the art; Viable solutions, Three dimensional computer graphics
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	29 May 2019 11:58
Last Modified:	29 May 2019 11:58
URI:	http://eprints.iisc.ac.in/id/eprint/62219

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