用无人机拍360影像，快速重建大型室内场景并生成新视角。
Scalable Indoor Novel-View Synthesis using Drone-Captured 360 Imagery with 3D Gaussian Splatting
- 用360度相机配合简单飞行轨迹，避免运动模糊。
- 通过分块重建和粗到精对齐，实现大场景高效合成。
- 相比之前方法，重建质量更高、速度更快，适合大规模室内建模。
大型复杂多层室内场景的重建与新视角生成是一项挑战性高且耗时的任务。以往方法虽采用无人机采集数据并使用辐射场进行重建，但仍存在局限：一是无人机前向相机需以不稳定的锯齿路径飞行，导致操控困难且图像出现运动模糊；二是多数辐射场方法难以扩展至大量图像。本文提出一种基于3D高斯点阵的高效可扩展管道，利用无人机拍摄的360视频实现室内新视角合成。360相机可在一个简单直线飞行轨迹下覆盖广泛视角，实现全面场景捕获。为应对大场景，我们设计了分而治之策略，自动将场景划分为多个可独立并行重建的小块，并提出粗到精对齐方法，无缝拼接各块形成完整场景。实验表明，本方法在重建质量（PSNR和SSIM）和计算效率上均显著优于先前方法。
原文摘要 · Abstract (English)
Scene reconstruction and novel-view synthesis for large, complex, multi-story, indoor scenes is a challenging and time-consuming task. Prior methods have utilized drones for data capture and radiance fields for scene reconstruction, both of which present certain challenges. First, in order to capture diverse viewpoints with the drone's front-facing camera, some approaches fly the drone in an unstable zig-zag fashion, which hinders drone-piloting and generates motion blur in the captured data. Secondly, most radiance field methods do not easily scale to arbitrarily large number of images. This paper proposes an efficient and scalable pipeline for indoor novel-view synthesis from drone-captured 360 videos using 3D Gaussian Splatting. 360 cameras capture a wide set of viewpoints, allowing for comprehensive scene capture under a simple straightforward drone trajectory. To scale our method to large scenes, we devise a divide-and-conquer strategy to automatically split the scene into smaller blocks that can be reconstructed individually and in parallel. We also propose a coarse-to-fine alignment strategy to seamlessly match these blocks together to compose the entire scene. Our experiments demonstrate marked improvement in both reconstruction quality, i.e. PSNR and SSIM, and computation time compared to prior approaches.
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