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Found 3 papers, 2 papers with code
End-to-end sensor modeling for LiDAR Point Cloud
The major problem with such approaches is that the amount of training data required for generalizing a machine learning model is big, and on the other hand LiDAR data annotation is very costly compared to other car sensors.
ShuffleSeg: Real-time Semantic Segmentation Network
It is shown that skip architecture in the decoding method provides the best compromise for the goal of real-time performance, while it provides adequate accuracy by utilizing higher resolution feature maps for a more accurate segmentation.
RTSeg: Real-time Semantic Segmentation Comparative Study
In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods.
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