Efficient Event Camera Volume System

2026-03-15

AI Summary

This work addresses the challenges of integrating event cameras into standard robotic systems, where their sparse asynchronous output and conventional time-binning approaches often introduce motion artifacts. The authors propose modeling the event stream as a continuous-time sequence of Dirac impulses, enabling artifact-free compression directly at event timestamps. They introduce an adaptive framework that dynamically selects among DCT, DTFT, or DWT transforms based on real-time event density, coupled with a domain-specific coefficient pruning strategy. Evaluated on MVSEC, the method achieves an EventSAM segmentation mIoU of 0.87—substantially outperforming voxel grids (0.44)—with only 1.5 ms latency using DCT, a 2.7× throughput improvement, and state-of-the-art reconstruction fidelity. The approach demonstrates strong generalization and is suitable for real-time deployment in ROS2.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Event cameras promise low latency and high dynamic range, yet their sparse output challenges integration into standard robotic pipelines. We introduce \nameframew (Efficient Event Camera Volume System), a novel framework that models event streams as continuous-time Dirac impulse trains, enabling artifact-free compression through direct transform evaluation at event timestamps. Our key innovation combines density-driven adaptive selection among DCT, DTFT, and DWT transforms with transform-specific coefficient pruning strategies tailored to each domain's sparsity characteristics. The framework eliminates temporal binning artifacts while automatically adapting compression strategies based on real-time event density analysis. On EHPT-XC and MVSEC datasets, our framework achieves superior reconstruction fidelity with DTFT delivering the lowest earth mover distance. In downstream segmentation tasks, EECVS demonstrates robust generalization. Notably, our approach demonstrates exceptional cross-dataset generalization: when evaluated with EventSAM segmentation, EECVS achieves mean IoU 0.87 on MVSEC versus 0.44 for voxel grids at 24 channels, while remaining competitive on EHPT-XC. Our ROS2 implementation provides real-time deployment with DCT processing achieving 1.5 ms latency and 2.7X higher throughput than alternative transforms, establishing the first adaptive event compression framework that maintains both computational efficiency and superior generalization across diverse robotic scenarios.

Problem

• event camera
• sparse output
• temporal binning artifacts
• adaptive compression
• robotic pipelines

Innovation

• event camera
• adaptive transform selection
• Dirac impulse train
• coefficient pruning
• temporal binning-free