This paper investigates "free lunch" strategies to boost the performance of lidar semantic scene completion (SSC) without requiring complex architectural redesigns. We first demonstrate that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmentors significantly improves the performance of existing architectures. By evaluating these models against an oracle, we establish that high-quality semantic priors are a primary driver of mIoU gains. Furthermore, we equip the input lidar scan with visibility information that distinguishes between empty and unknown spaces, which provides a secondary performance boost across the tested architectures. Using these simple enhancements, we observe that older models remain competitive with state-of-the-art systems, and can even outperform them. Our code is available at https://github.com/astra-vision/SSC-Priors.

翻译：本文研究了“免费午餐”策略，旨在无需复杂架构重构的情况下提升激光雷达语义场景补全（SSC）性能。我们首先证明，通过为输入点云赋予现成分割器生成的语义伪标签，可显著提升现有架构的表现。基于这些模型与理想基线的对比评估，我们确定高质量语义先验是实现mIoU提升的主要驱动因素。此外，我们为输入激光雷达扫描添加可见性信息以区分空白空间与未知空间，这为所有测试架构提供了次级性能提升。利用这些简单增强，我们发现早期模型仍能与现有最优系统保持竞争力，甚至超越后者。我们的代码开源于https://github.com/astra-vision/SSC-Priors。