Panoptic segmentation is a key enabler for robotic perception, as it unifies semantic understanding with object-level reasoning. However, the increasing complexity of state-of-the-art models makes them unsuitable for deployment on resource-constrained platforms such as mobile robots. We propose a novel approach called LiPS that addresses the challenge of efficient-to-compute panoptic segmentation with a lightweight design that retains query-based decoding while introducing a streamlined feature extraction and fusion pathway. It aims at providing a strong panoptic segmentation performance while substantially lowering the computational demands. Evaluations on standard benchmarks demonstrate that LiPS attains accuracy comparable to much heavier baselines, while providing up to 4.5 higher throughput, measured in frames per second, and requiring nearly 6.8 times fewer computations. This efficiency makes LiPS a highly relevant bridge between modern panoptic models and real-world robotic applications.

翻译：全景分割是机器人感知的关键技术，它将语义理解与对象级推理统一起来。然而，现有最先进模型日益复杂，使其难以部署在移动机器人等资源受限平台上。我们提出了一种名为LiPS的新方法，通过轻量级设计解决高效计算全景分割的挑战，该方法保留了基于查询的解码机制，同时引入了精简的特征提取与融合路径。其目标是在大幅降低计算需求的同时，提供强劲的全景分割性能。在标准基准上的评估表明，LiPS在达到与更重型基线相当的准确度的同时，实现了高达4.5倍的吞吐量提升（以每秒帧数衡量），并且所需计算量减少了近6.8倍。这种效率使LiPS成为连接现代全景模型与真实机器人应用之间的重要桥梁。