LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using
Multi-Scale Convolution Attention
- URL: http://arxiv.org/abs/2301.04275v3
- Date: Mon, 19 Jun 2023 00:35:59 GMT
- Title: LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using
Multi-Scale Convolution Attention
- Authors: Ben Ding
- Abstract summary: We propose a projection-based semantic segmentation network called LENet with an encoder-decoder structure for LiDAR-based semantic segmentation.
The encoder is composed of a novel multi-scale convolutional attention (MSCA) module with varying receptive field sizes to capture features.
We show that our proposed method is lighter, more efficient, and robust compared to state-of-the-art semantic segmentation methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: LiDAR-based semantic segmentation is critical in the fields of robotics and
autonomous driving as it provides a comprehensive understanding of the scene.
This paper proposes a lightweight and efficient projection-based semantic
segmentation network called LENet with an encoder-decoder structure for
LiDAR-based semantic segmentation. The encoder is composed of a novel
multi-scale convolutional attention (MSCA) module with varying receptive field
sizes to capture features. The decoder employs an Interpolation And Convolution
(IAC) mechanism utilizing bilinear interpolation for upsampling
multi-resolution feature maps and integrating previous and current dimensional
features through a single convolution layer. This approach significantly
reduces the network's complexity while also improving its accuracy.
Additionally, we introduce multiple auxiliary segmentation heads to further
refine the network's accuracy. Extensive evaluations on publicly available
datasets, including SemanticKITTI, SemanticPOSS, and nuScenes, show that our
proposed method is lighter, more efficient, and robust compared to
state-of-the-art semantic segmentation methods. Full implementation is
available at https://github.com/fengluodb/LENet.
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