Efficient Event Camera Volume System

TL;DR

This paper introduces an adaptive, artifact-free event camera data compression framework that models event streams as continuous-time impulses, combining multiple transforms and pruning strategies for real-time, high-fidelity data processing in robotics.

Contribution

The paper presents the first adaptive event compression framework that automatically selects and prunes transforms based on event density, achieving real-time performance and superior generalization.

Findings

Achieves lower earth mover distance with DTFT on datasets.

Demonstrates robust segmentation performance across datasets.

Provides real-time implementation with low latency and high throughput.

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.