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LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion
2022
Remote Sensing
Fast and accurate semantic scene understanding is essential for mobile robots to operate in complex environments. An emerging research topic, panoptic segmentation, serves such a purpose by performing the tasks of semantic segmentation and instance segmentation in a unified framework. To improve the performance of LiDAR-based real-time panoptic segmentation, this study proposes a spatiotemporal sequential data fusion strategy that fused points in "thing classes" based on accurate data
doi:10.3390/rs14081775
fatcat:qe4mhwschjej3gib5vkgdc5kdy