发表机构
Inria; ETH Zürich; LIGM, CNRS, Univ Gustave Eiffel, ENPC, IP Paris（法国国家信息与自动化研究所; 苏黎世联邦理工学院; 法国国家科学研究中心、古斯塔夫·埃菲尔大学、巴黎高科路桥学校、巴黎综合理工学院LIGM实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用语义伪标签和传感器可见性信息作为简单先验，无需复杂架构改动即可显著提升激光雷达语义场景补全性能，并在多个基准上验证了有效性。
AI 中文摘要
本文研究了在不进行复杂架构重新设计的情况下，提升现有激光雷达语义场景补全（SSC）网络性能的简单策略。事实是，在过去几年中，SSC方法主要追求架构创新，使得模型更加沉重和复杂，例如通过联合训练点云语义分割分支。在这项工作中，我们退一步，探索两种作为简单成分（可能带有噪声）的先验来改进现有方法：语义伪标签和传感器可见性信息。具体而言，我们将这两种信息直接作为额外输入提供给给定的SSC网络，仅需对原始架构进行最小程度的调整。我们首先证明，为输入点云赋予来自现成分割器的语义伪标签，能显著提升现有SSC模型的性能。实际上，通过将这些模型与一个oracle（理想参考）进行评估，我们确定高质量的语义先验是语义增益（mIoU）的主要驱动力，并且SSC模型可以仅使用地面真值语义训练一次，然后无需重新训练即可利用任何分割器进行推理。此外，我们为输入激光雷达点云配备可见性信息，该信息区分空空间（激光雷达与扫描点之间）和未知空间（视线之外），在测试的多种架构上提供了次要的性能提升。我们研究了表示可见性信息的数据设计空间，并使用自由空间标签的地面真值oracle来界定剩余的性能提升空间。在SemanticKITTI上，这些增强使较旧的模型在四种架构上与最先进的系统竞争，其中一种情况下甚至超越了它们。在SSCBench-nuScenes基准上，这两种先验也随着更稀疏的32线传感器转移。
英文摘要
This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring complex architectural redesigns. The fact is that, over the last years, SSC methods have mostly pursued architectural innovations, making the models heavier and more complex, e.g., by jointly training a point cloud semantic segmentation branch. In this work, we take a step back and explore two priors used as simple ingredients (possibly noisy) to improve existing approaches: semantic pseudo-labels and sensor visibility information. Concretely, we provide both kinds of information directly as additional inputs to a given SSC network, requiring only a minimal adaptation of the original architecture. We first demonstrate that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmenters significantly improves the performance of existing SSC models. In fact, by evaluating these models against an oracle, we establish that high-quality semantic priors are a primary driver of semantic gains (mIoU), and that the SSC model can be trained just once with ground-truth semantics and then exploited without retraining using any segmenter. Furthermore, we equip the input lidar point cloud with visibility information that distinguishes between empty spaces (between the lidar and a scanned point) and unknown spaces (outside of lines of sight), providing a secondary performance boost across the tested architectures. We study the design space of data for representing visibility information and bound the remaining headroom with a ground-truth oracle on the free-space labels. On SemanticKITTI, these enhancements make older models competitive with state-of-the-art systems across four architectures, in one case even outperforming them. On the SSCBench-nuScenes benchmark, both priors also transfer with the sparser 32-beam sensor.
CommentsExtended version of arXiv:2606.03992
