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

TL;DR

The paper introduces the DATA network, which systematically aligns features in collaborative perception to improve feature quality and fusion robustness amidst domain gaps and temporal delays, achieving state-of-the-art results.

Contribution

The paper proposes a novel framework with domain and temporal alignment modules to enhance feature fusion in collaborative perception under challenging conditions.

Findings

Achieves state-of-the-art performance on three datasets.

Maintains robustness with severe communication delays.

Effectively reduces domain gaps and compensates for transmission delays.

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.