Learning Local Displacements for Point Cloud Completion

Wang Y, Tan DJ, Navab N, Tombari F (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 1558-1567

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: New Orleans, LA, USA

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.00162

Abstract

We propose a novel approach aimed at object and semantic scene completion from a partial scan represented as a 3D point cloud. Our architecture relies on three novel layers that are used successively within an encoder-decoder structure and specifically developed for the task at hand. The first one carries out feature extraction by matching the point features to a set of pre-trained local descriptors. Then, to avoid losing individual descriptors as part of standard operations such as max-pooling, we propose an alternative neighbor-pooling operation that relies on adopting the feature vectors with the highest activations. Finally, upsampling in the decoder modifies our feature extraction in order to increase the output dimension. While this model is already able to achieve competitive results with the state of the art, we further propose a way to increase the versatility of our approach to process point clouds. To this aim, we introduce a second model that assembles our layers within a transformer architecture. We evaluate both architectures on object and indoor scene completion tasks, achieving state-of-the-art performance.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Google Inc. US United States (USA) (US)

How to cite

APA:

Wang, Y., Tan, D.J., Navab, N., & Tombari, F. (2022). Learning Local Displacements for Point Cloud Completion. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1558-1567). New Orleans, LA, USA: IEEE Computer Society.

MLA:

Wang, Yida, et al. "Learning Local Displacements for Point Cloud Completion." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 1558-1567.

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