Authors:
Hisato Hashimoto
and
Shuichi Enokida
Affiliation:
Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, Fukuoka, 820-8502, Japan
Keyword(s):
Semantic Segmentation, Point Cloud, Deep Learning.
Abstract:
The study delves into semantic segmentation’s role in recognizing regions within data, with a focus on images and 3D point clouds. While images from wide-angle cameras are prevalent, they falter in challenging environments like low light. In such cases, LIDAR (Laser Imaging Detection and Ranging), despite its lower resolution, excels. The combination of LIDAR and semantic segmentation proves effective for outdoor environment understanding. However, highly accurate models often demand substantial parameters, leading to computational challenges. Techniques like knowledge distillation and pruning offer solutions, though with possible accuracy trade-offs. This research introduces a strategy to merge feature descriptors, such as reflectance intensity and histograms, into the semantic segmentation model. This process balances accuracy and computational efficiency. The findings suggest that incorporating feature descriptors suits smaller models, while larger models can benefit from optimizi
ng computation and utilizing feature descriptors for recognition tasks. Ultimately, this research contributes to the evolution of resource-efficient semantic segmentation models for autonomous driving and similar fields.
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