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Shengran Qin

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Proceedings Article | 2 February 2023 Paper

AMBrnet: asymmetric multi-branch residual network for LiDAR semantic segmentation

Zizhong Wei, Fengshan Zou, Shengran Qin, Li Zhang

Proceedings Volume 12462, 1246223 (2023) https://doi.org/10.1117/12.2661083

KEYWORDS: Point clouds, Semantics, Convolution, Feature extraction, Education and training, LIDAR, Image processing, Mobile devices, 3D image processing, Voxels

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Existing point-based, sparse voxel-based, or hybrid point cloud processing methods require time-consuming neighborhood searches or sparse 3D convolutions, which consume a lot of time and computational resources. Therefore, it is difficult to run at high speed in real time on mobile devices. To this end, we reconstructed the internal structure based on RangeNet++¹ and designed an efficient and lightweight network named AMBrnet, still using the encoder-decoder architecture. An asymmetric multi-branch aggregation module is designed to directly aggregate the input information and provide rich information for subsequent step encoding. The dual-branch structure is introduced to combine with the encoder and decoder to strengthen the information encoding and decoding capabilities of the network. Weighted cross-entropy loss combined with Lovász-Softmax loss² is used to directly optimize the Jaccard index (IoU). In the network inference stage, structural reparameterization is introduced to ensure that the inference speed is improved based on the same accuracy, reducing the number of network parameters. We evaluate the proposed model on the SemanticKITTI dataset. The prediction accuracy is better than most existing networks, notably its inference speed is up to 43.9 Hz. Experimental data show that AMBrnet is well suited for real-time high-speed point cloud processing on outdoor mobile devices.

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