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|Title:||Efficient dense point cloud object reconstruction using deformation vector fields|
|Citation:||Lecture Notes in Artificial Intelligence, 2018 / Ferrari, V. (ed./s), vol.11216 LNCS, pp.508-524|
|Series/Report no.:||Lecture Notes in Computer Science; 11216|
|Conference Name:||European Conference on Computer Vision (ECCV) (8 Sep 2018 - 14 Sep 2018 : Munich, Germany)|
|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|
|Appears in Collections:||Aurora harvest 3|
Australian Institute for Machine Learning publications
Computer Science publications
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