Semantic Scene Completion (SSC) aims to perform geometric completion and semantic segmentation simultaneously. Despite the promising results achieved by existing studies, the inherently ill-posed nature of the task presents significant challenges in diverse driving scenarios. This paper introduces TALoS, a novel test-time adaptation approach for SSC that excavates the information available in driving environments. Specifically, we focus on that observations made at a certain moment can serve as Ground Truth (GT) for scene completion at another moment. Given the characteristics of the LiDAR sensor, an observation of an object at a certain location confirms both 1) the occupation of that location and 2) the absence of obstacles along the line of sight from the LiDAR to that point. TALoS utilizes these observations to obtain self-supervision about occupancy and emptiness, guiding the model to adapt to the scene in test time. In a similar manner, we aggregate reliable SSC predictions among multiple moments and leverage them as semantic pseudo-GT for adaptation. Further, to leverage future observations that are not accessible at the current time, we present a dual optimization scheme using the model in which the update is delayed until the future observation is available. Evaluations on the SemanticKITTI validation and test sets demonstrate that TALoS significantly improves the performance of the pre-trained SSC model. Our code is available at https://github.com/blue-531/TALoS.

翻译：语义场景补全（SSC）旨在同时执行几何补全与语义分割。尽管现有研究已取得有前景的结果，但该任务固有的不适定性在不同驾驶场景中仍带来显著挑战。本文提出TALoS，一种新颖的SSC测试时适应方法，旨在挖掘驾驶环境中可用的信息。具体而言，我们关注到在某一时刻的观测可作为另一时刻场景补全的真值（GT）。鉴于激光雷达传感器的特性，对某位置物体的观测同时确认了：1）该位置被占据，以及2）从激光雷达到该点的视线路径上无障碍物。TALoS利用这些观测获取关于占据与空白的自监督信号，引导模型在测试时适应场景。类似地，我们聚合多个时刻的可靠SSC预测，并将其作为语义伪真值用于适应过程。此外，为利用当前时刻无法获取的未来观测，我们提出一种采用延迟更新机制的双重优化方案，即模型更新延迟至未来观测可用时再进行。在SemanticKITTI验证集与测试集上的评估表明，TALoS显著提升了预训练SSC模型的性能。我们的代码公开于https://github.com/blue-531/TALoS。