Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion.

Ziyuan Qu, Omkar Vengurlekar, Mohamad Qadri, Kevin Zhang, Michael Kaess, Christopher Metzler, Suren Jayasuriya, Adithya Pediredla
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Abstract

Differentiable 3D-Gaussian splatting (GS) is emerging as a prominent technique in computer vision and graphics for reconstructing 3D scenes. GS represents a scene as a set of 3D Gaussians with varying opacities and employs a computationally efficient splatting operation along with analytical derivatives to compute the 3D Gaussian parameters given scene images captured from various viewpoints. Unfortunately, capturing surround view (360° viewpoint) images is impossible or impractical in many real-world imaging scenarios, including underwater imaging, rooms inside a building, and autonomous navigation. In these restricted baseline imaging scenarios, the GS algorithm suffers from a well-known 'missing cone' problem, which results in poor reconstruction along the depth axis. In this paper, we demonstrate that using transient data (from sonars) allows us to address the missing cone problem by sampling high-frequency data along the depth axis. We extend the Gaussian splatting algorithms for two commonly used sonars and propose fusion algorithms that simultaneously utilize RGB camera data and sonar data. Through simulations, emulations, and hardware experiments across various imaging scenarios, we show that the proposed fusion algorithms lead to significantly better novel view synthesis (5 dB improvement in PSNR) and 3D geometry reconstruction (60% lower Chamfer distance).

Z-Splat:用于相机-声纳融合的 Z 轴高斯拼接。
可微分三维高斯拼接(GS)是计算机视觉和图形学中重建三维场景的一项重要技术。GS 将场景表示为一组具有不同不透明度的三维高斯,并采用计算效率高的拼接操作和分析导数来计算从不同视角捕捉到的场景图像的三维高斯参数。遗憾的是,在现实世界的许多成像场景中,包括水下成像、建筑物内的房间和自主导航等,捕捉环视(360° 视角)图像是不可能或不切实际的。在这些受限的基线成像场景中,GS 算法存在众所周知的 "缺失锥 "问题,导致沿深度轴的重建效果不佳。在本文中,我们证明了使用瞬态数据(来自声纳)可以通过沿深度轴采样高频数据来解决锥体缺失问题。我们扩展了两种常用声纳的高斯拼接算法,并提出了同时利用 RGB 相机数据和声纳数据的融合算法。通过模拟、仿真和各种成像场景的硬件实验,我们发现所提出的融合算法能显著改善新视图合成(PSNR 提高 5 dB)和 3D 几何重建(倒角距离降低 60%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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