Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search results with varying backgrounds and illuminations. To enable systematic research progress on 3D reconstruction from casual image captures, we propose NAVI: a new dataset of category-agnostic image collections of objects with high-quality 3D scans along with per-image 2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D alignments allow us to extract accurate derivative annotations such as dense pixel correspondences, depth and segmentation maps. We demonstrate the use of NAVI image collections on different problem settings and show that NAVI enables more thorough evaluations that were not possible with existing datasets. We believe NAVI is beneficial for systematic research progress on 3D reconstruction and correspondence estimation. Project page: https://navidataset.github.io

翻译：神经重建领域的最新进展使得从随意拍摄的图像集合中实现高质量三维物体重建成为可能。现有技术大多针对较为简单的图像集合分析其进展——这类集合中，运动恢复结构（SfM）技术能够提供真实相机位姿。我们注意到，SfM技术在野外图像集合（例如背景与光照各异的图像搜索结果）中往往表现不佳。为了推动从随意拍摄图像进行三维重建的系统性研究进展，我们提出NAVI：一个全新的类别无关物体图像集合数据集，包含高质量三维扫描数据及逐图像二维-三维对齐信息，从而提供近乎完美的真实相机参数。这些二维-三维对齐使我们能够提取精确的衍生标注信息，如密集像素对应关系、深度图与分割图。我们展示了NAVI图像集合在不同问题设定下的应用，并证明NAVI支持了现有数据集无法实现的更全面评估。我们相信NAVI将有益于三维重建及对应关系估计领域的系统性研究进展。项目主页：https://navidataset.github.io