用全景相机实现大场景室内3D重建，精度显著提升。
IM360: Large-scale Indoor Mapping with 360 Cameras
- 结合全景图像与稀疏重建，优化关键帧匹配
- 纹理重建提升3.5 dB PSNR，效果优于现有方法
- 适合需要高精度室内三维建模的科研与工程应用
我们提出一种面向大规模室内场景的新型3D建图流程。针对大场景中普遍存在的遮挡和无纹理区域等挑战，IM360利用全景图像的宽视场特性，并将球面相机模型融入结构光从运动（SfM）流程中。所提出的SfM采用专为360图像设计的密集匹配特征，在图像配准方面表现优异。此外，借助基于网格的神经渲染技术，我们引入一种纹理优化方法，通过融合漫反射与镜面成分，精确还原视点相关的表面属性。在真实场景的大规模室内数据上评估表明，本方法在相机定位与配准任务上达到Matterport3D与Stanford2D3D数据集上的最先进水平，且在纹理化网格重建中实现3.5 dB的PSNR提升。
原文摘要 · Abstract (English)
We present a novel 3D mapping pipeline for large-scale indoor environments. To address the significant challenges in large-scale indoor scenes, such as prevalent occlusions and textureless regions, we propose IM360, a novel approach that leverages the wide field of view of omnidirectional images and integrates the spherical camera model into the Structure-from-Motion (SfM) pipeline. Our SfM utilizes dense matching features specifically designed for 360 images, demonstrating superior capability in image registration. Furthermore, with the aid of mesh-based neural rendering techniques, we introduce a texture optimization method that refines texture maps and accurately captures view-dependent properties by combining diffuse and specular components. We evaluate our pipeline on large-scale indoor scenes, demonstrating its effectiveness in real-world scenarios. In practice, IM360 demonstrates superior performance, achieving a 3.5 PSNR increase in textured mesh reconstruction. We attain state-of-the-art performance in terms of camera localization and registration on Matterport3D and Stanford2D3D.
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