论文标题
PCB旋转网络：重新思考自动驾驶场景中激光雷达语义细分的随机抽样
PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene
论文作者
论文摘要
大规模发光点云的快速有效的语义分割是自动驾驶中的一个基本问题。为了实现这一目标，现有的基于点的方法主要选择采用随机抽样策略来处理大规模点云。但是，我们的数量和定性研究发现，随机抽样可能不适合自主驾驶场景，因为雷达点遵循整个空间的不均匀甚至长尾分布，这阻止了模型从不同距离范围内的点捕获足够的信息，并降低了模型的学习能力。为了减轻这个问题，我们提出了一种新的极性缸平衡的随机抽样方法，该方法使下采样的点云能够保持更平衡的分布并在不同的空间分布下提高分割性能。此外，引入了采样一致性损失，以进一步提高分割性能并降低模型在不同的采样方法下的方差。广泛的实验证实，我们的方法在Semantickitti和Semanticposs基准测试中都产生了出色的性能，分别提高了2.8％和4.0％。源代码可在https://github.com/huixiancheng/pcb-randnet上找到。
Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving. To achieve this goal, the existing point-based methods mainly choose to adopt Random Sampling strategy to process large-scale point clouds. However, our quantative and qualitative studies have found that Random Sampling may be less suitable for the autonomous driving scenario, since the LiDAR points follow an uneven or even long-tailed distribution across the space, which prevents the model from capturing sufficient information from points in different distance ranges and reduces the model's learning capability. To alleviate this problem, we propose a new Polar Cylinder Balanced Random Sampling method that enables the downsampled point clouds to maintain a more balanced distribution and improve the segmentation performance under different spatial distributions. In addition, a sampling consistency loss is introduced to further improve the segmentation performance and reduce the model's variance under different sampling methods. Extensive experiments confirm that our approach produces excellent performance on both SemanticKITTI and SemanticPOSS benchmarks, achieving a 2.8% and 4.0% improvement, respectively. The source code is available at https://github.com/huixiancheng/PCB-RandNet.
