Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception

TL;DR

Long-SCOPE is a novel sparse framework for cooperative 3D perception that enhances long-range sensing and robustness in autonomous driving, addressing computational and feature association challenges.

Contribution

It introduces Geometry-guided Query Generation and a learnable Context-Aware Association for improved long-distance perception under noise.

Findings

Achieves state-of-the-art performance on V2X-Seq and Griffin datasets.

Excels in 100-150 m long-range perception scenarios.

Maintains competitive computation and communication costs.

Abstract

Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks: the quadratic computational scaling of dense BEV representations and the fragility of feature association mechanisms under significant observation and alignment errors. To overcome these limitations, we introduce Long-SCOPE, a fully sparse framework designed for robust long-distance cooperative 3D perception. Our method features two novel components: a Geometry-guided Query Generation module to accurately detect small, distant objects, and a learnable Context-Aware Association module that robustly matches cooperative queries despite severe positional noise. Experiments on the V2X-Seq and Griffin datasets validate that Long-SCOPE achieves state-of-the-art performance, particularly in challenging 100-150 m long-range settings, while maintaining highly competitive computation and communication costs.