Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/116856
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Type: | Conference paper |
Title: | Efficient dense point cloud object reconstruction using deformation vector fields |
Author: | Li, K. Pham, T. Zhan, H. Reid, I. |
Citation: | Lecture Notes in Artificial Intelligence, 2018 / Ferrari, V. (ed./s), vol.11216 LNCS, pp.508-524 |
Publisher: | Springer Nature |
Publisher Place: | Switzerland |
Issue Date: | 2018 |
Series/Report no.: | Lecture Notes in Computer Science; 11216 |
ISBN: | 9783030012571 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | European Conference on Computer Vision (ECCV) (8 Sep 2018 - 14 Sep 2018 : Munich, Germany) |
Editor: | Ferrari, V. |
Statement of Responsibility: | Kejie Li, Trung Pham, Huangying Zhan, Ian Reid |
Abstract: | Some existing CNN-based methods for single-view 3D object reconstruction represent a 3D object as either a 3D voxel occupancy grid or multiple depth-mask image pairs. However, these representations are inefficient since empty voxels or background pixels are wasteful. We propose a novel approach that addresses this limitation by replacing masks with “deformation-fields”. Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object. Each surface comprises a depth-map and corresponding deformation-field that ensures every pixel-depth pair in the depth-map lies on the object surface. These surfaces are then fused to form the full 3D shape. During training we use a combination of per-view loss and multi-view losses. The novel multi-view loss encourages the 3D points back-projected from a particular view to be consistent across views. Extensive experiments demonstrate the efficiency and efficacy of our method on single-view 3D object reconstruction. |
Keywords: | 3D object reconstruction; dense point clouds; deep learning |
Rights: | © Springer Nature Switzerland AG 2018 |
DOI: | 10.1007/978-3-030-01258-8_31 |
Grant ID: | http://purl.org/au-research/grants/arc/FL130100102 http://purl.org/au-research/grants/arc/CE140100016 |
Published version: | http://dx.doi.org/10.1007/978-3-030-01258-8_31 |
Appears in Collections: | Aurora harvest 3 Australian Institute for Machine Learning publications Computer Science publications |
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