aiqbench analysis
LiPS introduces a lightweight architecture for panoptic segmentation that significantly reduces computational costs while maintaining high accuracy, making advanced robotic perception feasible on resource-constrained mobile platforms. By streamlining feature extraction and fusion pathways, this method bridges the gap between heavy state-of-the-art models and real-world deployment requirements without sacrificing performance.
LiPS: Lightweight Panoptic Segmentation for Resource-Constrained Robotics
arXiv:2604.00634v3 Announce Type: replace Abstract: 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.
Abstract: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.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2604.00634 [cs.RO]
(or arXiv:2604.00634v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2604.00634 Focus to learn more arXiv-issued DOI via DataCite
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