EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams

2026-08-09

AI Summary

This work addresses the challenge of balancing reconstruction accuracy and efficiency in event-based 3D reconstruction without initial pose estimates. We propose EvTrajGS, a novel framework that integrates continuous-time trajectory modeling with 3D Gaussian Splatting, enabling temporally consistent, high-fidelity scene reconstruction through joint optimization of camera poses and geometry. The method introduces a temporally coupled pose representation and an adaptive event sampling strategy with loss reweighting, which mitigates drift accumulation while avoiding the high computational overhead of traditional SLAM pipelines. Experiments on both synthetic and real-world datasets demonstrate substantial improvements over state-of-the-art approaches, achieving a 3.8 dB gain in PSNR, a 0.1 increase in SSIM, and over 40% reduction in ATE RMSE, all while maintaining computational efficiency.

Abstract

Event cameras, with high temporal resolution, high dynamic range, and asynchronous sensing characteristics, have shown great potential for dense 3D reconstruction. Traditional reconstruction methods based on off-the-shelf pose estimates achieve high efficiency but produce low-fidelity results, as inaccurate pose initialization introduces cumulative reconstruction errors. In contrast, recent SLAM-style methods stabilize joint pose-scene optimization through incremental tracking and mapping, yielding higher reconstruction fidelity at the expense of considerable computational overhead. To address this trade-off, this paper presents EvTrajGS, an accurate and efficient 3D Gaussian Splatting framework for unposed event streams. Our method enables reliable joint pose-scene optimization initialized from coarse pose priors, eliminating the need for computationally expensive SLAM-style pipelines. EvTrajGS parameterizes camera motion as a continuous-time trajectory initialized from discrete camera poses, providing a unified representation for pose refinement. We then aggregate adjacent trajectory states into a temporally coupled pose, promoting temporally consistent pose updates during joint optimization. Additionally, we introduce a loss-reweighted event sampling strategy to adaptively emphasize temporally under-reconstructed intervals. Extensive experiments on both synthetic and real-world datasets demonstrate that EvTrajGS outperforms state-of-the-art methods in terms of both geometric reconstruction quality and pose estimation accuracy, achieving 3.8 dB higher PSNR, 0.1 higher SSIM, and over 40\% lower ATE RMSE while retaining high computational efficiency.

Problem

• event cameras
• 3D reconstruction
• pose estimation
• Gaussian Splatting
• unposed streams

Innovation

• Event Camera
• 3D Gaussian Splatting
• Unposed Event Streams
• Continuous-time Trajectory
• Joint Pose-Scene Optimization