Abstract
A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of-a-kind framework for fulfilling multi-task, multi-dataset, multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity, we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces, namely data, feature, and label spaces, during the training. As a result, M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably, using a shared set of parameters, M3Net achieves 75.1%, 83.1%, and 72.4% mIoU scores, respectively, on the official benchmarks of SemanticKITTI, nuScenes, and Waymo Open.
Abstract (translated)
一个统一且具有强大稳健性和泛化能力的LiDAR分割模型对于安全自动驾驶感知是可取的。本文提出M3Net，一个独一无二的框架，用于通过仅使用一组参数实现多任务、多数据集和多模态LiDAR分割。为了更好地利用数据量和多样性，我们首先将不同场景下获取的大规模驾驶数据集合并，然后进行数据、特征和标签空间的归一化训练。结果，M3Net能够驯服训练中最具竞争力的LiDAR分割模型的异质数据。在十二个LiDAR分割数据集上的广泛实验证实了我们的有效性。值得注意的是，使用共享参数，M3Net在SemanticKITTI、nuScenes和Waymo Open的官方基准测试中都取得了75.1%、83.1%和72.4%的mIoU分数。
URL
https://arxiv.org/abs/2405.01538
