Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. Deformable odometry and SLAM pipelines, which tackle the most challenging scenario of exploratory trajectories, suffer from a lack of robustness and proper quantitative evaluation methodologies. To tackle this issue with a common benchmark, we introduce the Drunkard's Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality. We further present a novel deformable odometry method, dubbed the Drunkard's Odometry, which decomposes optical flow estimates into rigid-body camera motion and non-rigid scene deformations. In order to validate our data, our work contains an evaluation of several baselines as well as a novel tracking error metric which does not require ground truth data. Dataset and code: https://davidrecasens.github.io/TheDrunkard'sOdometry/

翻译：在可变形场景中估计相机运动是一个复杂且开放的研究挑战。大多数现有的非刚体运动恢复结构技术假设除了变形场景部分外，还能观察到静态场景部分，以建立锚定参考。然而，这一假设在某些相关应用案例（如内窥镜）中并不成立。应对探索性轨迹这一最具挑战性场景的可变形里程计与SLAM流水线，缺乏鲁棒性和适当的定量评估方法。为了解决这一问题并提供通用基准，我们推出了“醉酒者数据集”，这是一个针对变形环境中视觉导航与重建的挑战性合成数据集合。该数据集是首个包含大量探索性相机轨迹（在时间上每个表面均表现出非刚性变形的3D场景中）并带有地面真值的数据集。在逼真的3D建筑中进行模拟使我们能够获得大量数据及地面真值标签，包括高分辨率与高质量的相机位姿、RGB图像与深度、光流及法线贴图。我们还提出了一种新颖的可变形里程计方法，称为“醉酒者里程计”，该方法将光流估计分解为刚体相机运动与非刚性场景变形。为验证我们的数据，本文包含了对多个基线方法的评估，以及一种无需地面真值数据的新型跟踪误差度量。数据集与代码：https://davidrecasens.github.io/TheDrunkard'sOdometry/