Back to Search
Start Over
CAS-NET: Cascade attention-based sampling neural network for point cloud simplification
- Publication Year :
- 2023
- Publisher :
- IEEE, 2023.
-
Abstract
- Point cloud sampling can reduce storage requirements and computation costs for various vision tasks. Traditional sampling methods, such as farthest point sampling, are not geared towards downstream tasks and may fail on such tasks. In this paper, we propose a cascade attention-based sampling network (CAS-Net), which is end-to-end trainable. Specifically, we propose an attention-based sampling module (ASM) to capture the semantic features and preserve the geometry of the original point cloud. Experimental results on the ModelNet40 dataset show that CAS-Net outperforms state-of-the-art methods in a sampling-based point cloud classification task, while preserving the geometric structure of the sampled point cloud.
- Subjects :
- Point clouds
Attention-based sampling
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.od.......909..ccbe28c215a835d59b30253baaf55f75