IncEventGS: Pose-Free Gaussian Splatting from a Single Event Camera
Keywords: 3D Gaussian, Event Camera
TL;DR: In this work, we present IncEventGS, a high-quality 3D Gaussian using a single event camera, without requiring ground truth camera poses.
Abstract: Implicit neural representation and explicit 3D Gaussian Splatting (3D-GS) for novel
view synthesis have achieved remarkable progress with frame-based camera (e.g.
RGB and RGB-D cameras) recently. Compared to frame-based camera, a novel
type of bio-inspired visual sensor, i.e. event camera, has demonstrated advantages
in high temporal resolution, high dynamic range, low power consumption and
low latency. Due to its unique asynchronous and irregular data capturing process,
limited work has been proposed to apply neural representation or 3D Gaussian
splatting for an event camera. In this work, we present IncEventGS, an incremental
3D Gaussian Splatting reconstruction algorithm with a single event camera. To
recover the 3D scene representation incrementally, we exploit the tracking and
mapping paradigm of conventional SLAM pipelines for IncEventGS. Given the
incoming event stream, the tracker firstly estimates an initial camera motion based
on prior reconstructed 3D-GS scene representation. The mapper then jointly refines
both the 3D scene representation and camera motion based on the previously
estimated motion trajectory from the tracker. The experimental results demonstrate
that IncEventGS delivers superior performance compared to prior NeRF-based
methods and other related baselines, even we do not have the ground-truth camera poses.
Furthermore, our method can also deliver better performance compared to state-of-
the-art event visual odometry methods in terms of camera motion estimation.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 3822
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