Send Less, Perceive More: Masked Quantized Point Cloud Communication for Loss-Tolerant Collaborative Perception

TL;DR

QPoint2Comm is introduced, a quantized point-cloud communication framework that dramatically reduces bandwidth while preserving high-fidelity 3D information and employs a masked training strategy that simulates random packet loss, allowing the model to maintain strong performance even under severe transmission failures.

Abstract

Collaborative perception allows connected vehicles to overcome occlusions and limited viewpoints by sharing sensory information. However, existing approaches struggle to achieve high accuracy under strict bandwidth constraints and remain highly vulnerable to random transmission packet loss. We introduce QPoint2Comm, a quantized point-cloud communication framework that dramatically reduces bandwidth while preserving high-fidelity 3D information. Instead of transmitting intermediate features, QPoint2Comm directly communicates quantized point-cloud indices using a shared codebook, enabling efficient reconstruction with lower bandwidth than feature-based methods. To ensure robustness to possible communication packet loss, we employ a masked training strategy that simulates random packet loss, allowing the model to maintain strong performance even under severe transmission failures. In addition, a cascade attention fusion module is proposed to enhance multi-vehicle information integration. Extensive experiments on both simulated and real-world datasets demonstrate that QPoint2Comm sets a new state of the art in accuracy, communication efficiency, and resilience to packet loss.

Figures (14)

• Figure 1: Comparison between our proposed quantized point cloud-based framework and existing feature-based methods for collaborative perception.
• Figure 2: The Discrete Point Cloud Representation (DCR) module is trained to encode point clouds into discrete index sequences and to faithfully reconstruct them via a shared codebook.
• Figure 3: Overview of the proposed collaborative perception framework, consisting of Discrete Point Cloud Representation (\ref{['subsec:DCR']}) for point cloud encoding and decoding, Packet-loss Tolerant Design (\ref{['subsec:PTD']}) to enable communication with missing packets, Feature Fusion and Bounding Boxes Generation (\ref{['subsec:FFBBG']}) module for generating final bounding boxes.
• Figure 4: The Feature Fusion and Bounding Boxes generation.
• Figure 5: (a) The Pyramid-scale Fusion. (b) The Cascade Attention Fusion (CAF).