轻量级全景分割模型，让机器人在低算力下也能高效感知场景。
LiPS: Lightweight Panoptic Segmentation for Resource-Constrained Robotics
- 采用查询解码+精简特征融合路径，兼顾精度与效率。
- 在标准数据集上达到与重型模型相当的准确率，速度提升4.5倍。
- 适合部署在移动机器人等资源受限设备，实用性强。
全景分割是机器人感知的关键技术，能统一语义理解与物体级推理。然而，当前先进模型复杂度高，难以在移动机器人等资源受限平台部署。本文提出LiPS，一种轻量级全景分割方法，在保留查询解码机制的同时，设计了简化版特征提取与融合路径，显著降低计算开销。在标准基准测试中，LiPS性能接近更重的基线模型，帧率提升最高达4.5倍（以帧每秒计），计算量减少近6.8倍。该效率使其成为现代全景模型与真实机器人应用之间的有效桥梁。
原文摘要 · Abstract (English)
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.
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