4.7 Article

KASiam: Keypoints-Aligned Siamese Network for the Completion of Partial TLS Point Clouds

Journal

REMOTE SENSING
Volume 14, Issue 15, Pages -

Publisher

MDPI
DOI: 10.3390/rs14153617

Keywords

TLS point cloud completion; siamese network; attention mechanism; K-Nearest Neighbors; Chamfer Distance

Funding

  1. National Natural Science Foundation of China [61871386]

Ask authors/readers for more resources

This paper proposes a keypoints-aligned siamese network for completing partial TLS point clouds, which learns prior geometric information of complete shapes and establishes long-range geometric relationships, resulting in improved point cloud completion.
Completing point clouds from partial terrestrial laser scannings (TLS) is a fundamental step for many 3D visual applications, such as remote sensing, digital city and autonomous driving. However, existing methods mainly followed an ordinary auto-encoder architecture with only partial point clouds as inputs, and adopted K-Nearest Neighbors (KNN) operations to extract local geometric features, which takes insufficient advantage of input point clouds and has limited ability to extract features from long-range geometric relationships, respectively. In this paper, we propose a keypoints-aligned siamese (KASiam) network for the completion of partial TLS point clouds. The network follows a novel siamese auto-encoder architecture, to learn prior geometric information of complete shapes by aligning keypoints of complete-partial pairs during the stage of training. Moreover, we propose two essential blocks cross-attention perception (CAP) and self-attention augment (SAA), which replace KNN operations with attention mechanisms and are able to establish long-range geometric relationships among points by selecting neighborhoods adaptively at the global level. Experiments are conducted on widely used benchmarks and several TLS data, which demonstrate that our method outperforms other state-of-the-art methods by a 4.72% reduction of the average Chamfer Distance of categories in PCN dataset at least, and can generate finer shapes of point clouds on partial TLS data.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available