V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detection

TL;DR

V2X-DGPE is a novel framework for collaborative 3D object detection that effectively mitigates domain gaps and pose errors using knowledge distillation, feature compensation, and deformable attention, achieving state-of-the-art results.

Contribution

It introduces a comprehensive V2X perception framework that addresses domain gaps and pose errors with innovative modules and techniques, improving robustness and accuracy.

Findings

Outperforms existing methods on DAIR-V2X dataset

Achieves state-of-the-art detection performance

Effectively reduces feature distribution gaps and compensates pose errors

Abstract

In V2X collaborative perception, the domain gaps between heterogeneous nodes pose a significant challenge for effective information fusion. Pose errors arising from latency and GPS localization noise further exacerbate the issue by leading to feature misalignment. To overcome these challenges, we propose V2X-DGPE, a high-accuracy and robust V2X feature-level collaborative perception framework. V2X-DGPE employs a Knowledge Distillation Framework and a Feature Compensation Module to learn domain-invariant representations from multi-source data, effectively reducing the feature distribution gap between vehicles and roadside infrastructure. Historical information is utilized to provide the model with a more comprehensive understanding of the current scene. Furthermore, a Collaborative Fusion Module leverages a heterogeneous self-attention mechanism to extract and integrate heterogeneous representations from vehicles and infrastructure. To address pose errors, V2X-DGPE introduces a deformable attention mechanism, enabling the model to adaptively focus on critical parts of the input features by dynamically offsetting sampling points. Extensive experiments on the real-world DAIR-V2X dataset demonstrate that the proposed method outperforms existing approaches, achieving state-of-the-art detection performance. The code is available at https://github.com/wangsch10/V2X-DGPE.