CoLC: Communication-Efficient Collaborative Perception with LiDAR Completion

TL;DR

Experiments demonstrate that the proposed communication-efficient early Collaborative perception framework that incorporates LiDAR Completion to restore scene completeness under sparse transmission achieves superior perception-communication trade-offs and remains robust under heterogeneous model settings.

Abstract

Collaborative perception empowers autonomous agents to share complementary information and overcome perception limitations. While early fusion offers more perceptual complementarity and is inherently robust to model heterogeneity, its high communication cost has limited its practical deployment, prompting most existing works to favor intermediate or late fusion. To address this, we propose a communication-efficient early Collaborative perception framework that incorporates LiDAR Completion to restore scene completeness under sparse transmission, dubbed as CoLC. Specifically, the CoLC integrates three complementary designs. First, each neighbor agent applies Foreground-Aware Point Sampling (FAPS) to selectively transmit informative points that retain essential structural and contextual cues under bandwidth constraints. The ego agent then employs Completion-Enhanced Early Fusion (CEEF) to reconstruct dense pillars from the received sparse inputs and adaptively fuse them with its own observations, thereby restoring spatial completeness. Finally, the Dense-Guided Dual Alignment (DGDA) strategy enforces semantic and geometric consistency between the enhanced and dense pillars during training, ensuring consistent and robust feature learning. Experiments on both simulated and real-world datasets demonstrate that CoLC achieves superior perception-communication trade-offs and remains robust under heterogeneous model settings. The code is available at https://github.com/CatOneTwo/CoLC.

Figures (20)

• Figure 1: Comparative performance of early fusion when transmitting foreground (FG) points, foreground with surrounding (FG+SUR) points, background (BG) points, or all points. Transmitting FG or FG+SUR points leads to significant degradation due to missing contextual cues, demonstrating the necessity of incorporating both FG and BG points in early fusion.
• Figure 2: The overall architecture of CoLC. (a) Each neighbor agent applies Foreground-Aware Point Sampling (FAPS) to select and transmit informative points to the ego agent for efficient communication. (b) The ego agent performs Completion-Enhanced Early Fusion (CEEF), where the received sparse points are pillarized $\Phi_{\text{p}}(\cdot)$, completed into dense pillars, and adaptively fused with the initial sparse fusion to enhance structural detail. (c) During training, Dense-Guided Dual Alignment (DGDA) enforces semantic distribution and geometric direction consistency, further improving the alignment of the fused representation.
• Figure 3: VQ-based LiDAR completion.
• Figure 4: Adaptive complementary fusion.
• Figure : (a) Accuracy-Bandwidth.