A U-Net variant using channel-wise 1D convolutions and pixel-shuffle operations substantially reduces model size, but the claim of accuracy preservation is contradicted by the paper's own results on general datasets.
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
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abstract
LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain, including point-based, range-image-based, polar-coordinate-based, and hybrid strategies. Among these, range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However, they face a significant challenge known as the ``many-to-one'' problem caused by the range image's limited horizontal and vertical angular resolution. As a result, around 20% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the ``many-to-one'' issue. We evaluated the approach on two benchmarks and demonstrated that the plug-in post-processing technique is generic and can be applied to various networks.
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OneNet: A Channel-Wise 1D Convolutional U-Net
A U-Net variant using channel-wise 1D convolutions and pixel-shuffle operations substantially reduces model size, but the claim of accuracy preservation is contradicted by the paper's own results on general datasets.