Adaptive Multi-Modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications

Exploring foci of: doi.org Adaptive Multi-Modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications July 2020 • Pai Peng, Keke Geng, Guodong Yin, Yanbo Lu, Weichao Zhuang, Shuaipeng Liu <title>Abstract</title> This paper aims to develop an end-to-end sharpening mixture of experts (SMoE) fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions. Three original contributions make our work distinctive from the existing relevant literature. First, we introduce the Complex KITTI dataset which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps, this dataset is fine annotated in ins… Open Article Page

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