TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2309.07849v3
• Date: Sun, 14 Apr 2024 17:29:46 GMT
• Title: TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
• Authors: Rong Li, ShiJie Li, Xieyuanli Chen, Teli Ma, Juergen Gall, Junwei Liang,
• Abstract summary: We present TFNet, a range-image-based LiDAR semantic segmentation method. 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.
• Score: 22.827914249161875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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