DATA: Domain-And-Time Alignment for High-Quality Feature Fusion in Collaborative Perception

2025-07-24

AI Summary

In collaborative perception, hardware heterogeneity induces feature-domain shift, while communication latency causes temporal misalignment—jointly degrading feature quality and accumulating cross-node errors. To address these challenges at the feature-level fusion stage, we propose a systematic alignment framework: (1) a consistency-preserving domain alignment module mitigates inter-device feature distribution discrepancies; (2) a progressive temporal alignment module corrects dynamic timing offsets via multi-scale motion modeling and two-stage compensation; and (3) an observability-constrained discriminator and instance-aware hierarchical aggregation strategy enhance semantic consistency. Evaluated on three benchmark datasets, our method achieves state-of-the-art performance and demonstrates significantly improved robustness under high communication latency and pose estimation errors.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Feature-level fusion shows promise in collaborative perception (CP) through balanced performance and communication bandwidth trade-off. However, its effectiveness critically relies on input feature quality. The acquisition of high-quality features faces domain gaps from hardware diversity and deployment conditions, alongside temporal misalignment from transmission delays. These challenges degrade feature quality with cumulative effects throughout the collaborative network. In this paper, we present the Domain-And-Time Alignment (DATA) network, designed to systematically align features while maximizing their semantic representations for fusion. Specifically, we propose a Consistency-preserving Domain Alignment Module (CDAM) that reduces domain gaps through proximal-region hierarchical downsampling and observability-constrained discriminator. We further propose a Progressive Temporal Alignment Module (PTAM) to handle transmission delays via multi-scale motion modeling and two-stage compensation. Building upon the aligned features, an Instance-focused Feature Aggregation Module (IFAM) is developed to enhance semantic representations. Extensive experiments demonstrate that DATA achieves state-of-the-art performance on three typical datasets, maintaining robustness with severe communication delays and pose errors. The code will be released at https://github.com/ChengchangTian/DATA.

Problem

• Address domain gaps from hardware diversity and deployment conditions
• Mitigate temporal misalignment caused by transmission delays
• Enhance semantic feature quality for collaborative perception fusion

Innovation

• Consistency-preserving Domain Alignment Module reduces gaps
• Progressive Temporal Alignment Module handles delays
• Instance-focused Feature Aggregation enhances semantics