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KPRNet: Improving projection-based LiDAR semantic segmentation
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Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation to achieve a better accuracy in the task of segmenting LiDAR scans. KPRNet improves the convolutional neural network architecture of 2D projection methods and utilizes KPConv to replace the commonly used post-processing techniques with a learnable point-wise component which allows us to obtain more accurate 3D labels. With these improvements our model outperforms the current best method on the SemanticKITTI benchmark, reaching an mIoU of 63.1.
Forward citations
Cited by 2 Pith papers
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Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing
A 2.78M-parameter student distilled from a 46.16M-parameter Point Transformer V3 teacher stays within a few mIoU points on LiDAR segmentation while being roughly 16x smaller and 1.6x faster.
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PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation
PC-BEV fuses polar and Cartesian BEV features with precomputed remaps, achieving real-time LiDAR semantic segmentation that beats several range-view fusion baselines.
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