LiPS: Lightweight Panoptic Segmentation for Resource-Constrained Robotics

TL;DR

LiPS is a lightweight panoptic segmentation method designed for resource-constrained robots, achieving high accuracy with significantly reduced computational requirements and increased processing speed.

Contribution

The paper introduces LiPS, a novel lightweight panoptic segmentation approach that maintains performance while drastically reducing computational load for robotic applications.

Findings

LiPS attains accuracy comparable to heavier models.

LiPS provides up to 4.5 times higher throughput.

LiPS requires nearly 6.8 times fewer computations.

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.