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

2026-04-10

AI Summary

This work addresses the high computational cost of dense bird’s-eye-view (BEV) representations and the susceptibility of feature association to observation and alignment errors in long-range vehicle-infrastructure cooperative 3D perception. To this end, the authors propose a fully sparse collaborative perception framework that eliminates dense BEV construction altogether. The method introduces a geometry-guided query generation mechanism to accurately localize distant small objects and designs a context-aware, learnable feature association module to significantly enhance matching robustness under severe positional noise. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance on the V2X-Seq and Griffin datasets, with particularly notable gains in challenging scenarios at distances of 100–150 meters, while substantially reducing both computational and communication overhead.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Cooperative 3D perception
• long-range
• sparse representation
• feature association
• V2X communication

Innovation

• Fully Sparse
• Long-Range Cooperative Perception
• Geometry-guided Query Generation
• Context-Aware Association
• V2X 3D Perception