Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation

Hanz Cuevas-Velasquez, Antonio Javier Gallego, Robert B Fisher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We present an innovative two-headed attention layer that combines geometric and latent features to segment a 3D scene into semantically meaningful subsets. Each head combines local and global information, using either the geometric or latent features, of a neighborhood of points and uses this information to learn better local relationships. This Geometric-Latent attention layer (Ge-Latto) is combined with a sub-sampling strategy to capture global features. Our method is invariant to permutation thanks to the use of shared-MLP layers, and it can also be used with point clouds with varying densities because the local attention layer does not depend on the neighbor order. Our proposal is simple yet robust, which allows it to achieve competitive results in the ShapeNetPart and ModelNet40 datasets, and the state-of-the-art when segmenting the complex dataset S3DIS, with 69.2% IoU on Area 5, and 89.7% overall accuracy using K-fold cross-validation on the 6 areas.
Original languageEnglish
Title of host publicationProceedings of the 32nd British Machine Vision Conference
PublisherBritish Machine Vision Conference
Number of pages14
Publication statusPublished - 25 Nov 2021
EventThe 32nd British Machine Vision Conference - Virtual
Duration: 22 Nov 202125 Nov 2021
https://www.bmvc2021.com/

Conference

ConferenceThe 32nd British Machine Vision Conference
Abbreviated titleBMVC 2021
Period22/11/2125/11/21
Internet address

Fingerprint

Dive into the research topics of 'Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation'. Together they form a unique fingerprint.

Cite this