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实现了从随意拍摄的图像集合中进行高质量3D物体重建。当前技术主要在相对简单的图像集合上分析其进展，这些集合中的运动恢复结构（SfM）技术能够提供地面真值（GT）相机姿态。我们注意到，SfM技术在非受控图像集合（例如背景和光照多变的图像搜索结果）中往往失效。为了推动随意图像采集场景下3D重建的系统性研究进展，我们提出NAVI：一个新的类别无关物体图像集合数据集，包含高质量3D扫描以及每张图像的二维到三维对齐，从而提供近乎完美的GT相机参数。这些二维到三维对齐使我们能够提取精确的衍生标注，例如密集像素对应关系、深度图和分割图。我们展示了NAVI图像集合在不同问题设定下的应用，并表明NAVI能够实现现有数据集无法实现的更全面评估。我们相信NAVI有助于推动3D重建与对应关系估计领域的系统性研究进展。项目页面：https://navidataset.github.io