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This paper introduces LiPS, a lightweight panoptic segmentation model designed for resource-constrained robotic platforms. LiPS achieves efficiency through a streamlined feature extraction and fusion pathway while retaining query-based decoding. Experiments on standard benchmarks show that LiPS achieves comparable accuracy to heavier baselines with up to 4.5x higher throughput and 6.8x fewer computations.
Resource-strapped robots can now achieve near state-of-the-art panoptic segmentation without sacrificing accuracy, thanks to a novel lightweight architecture that slashes computational costs.
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.
