我们是否在深度单目视觉SLAM中掌握了尺度？ScaleMaster数据集和基准
Have We Mastered Scale in Deep Monocular Visual SLAM? The ScaleMaster Dataset and Benchmark
AI总结：
本文提出ScaleMaster数据集和基准，旨在评估深度单目视觉SLAM在大规模室内环境中对尺度一致性问题的处理能力，揭示现有系统在真实场景中的不足。
摘要：
近年来，深度单目视觉同时定位与建图（SLAM）在准确性和密集重建能力方面取得了显著进展，但其在大规模室内环境中对尺度不一致的鲁棒性仍鲜有研究。现有基准仅限于房间尺度或结构简单的场景，未能充分解决会话内尺度漂移和会话间尺度模糊等问题。为此，我们引入了ScaleMaster数据集，这是首个专门设计用于评估在多层结构、长轨迹、重复视图和低纹理区域等挑战性场景下尺度一致性的情景基准。我们系统分析了当前最先进的深度单目视觉SLAM系统对尺度不一致的脆弱性，提供了定量和定性评估。关键在于，我们的分析超越了传统的轨迹指标，采用Chamfer距离等指标对地图到地图的质量进行直接评估，与高保真3D地面真实值对比。我们的结果表明，尽管最近的深度单目视觉SLAM系统在现有基准上表现强劲，但在现实中的大规模室内环境中却面临严重的尺度相关失败。通过发布ScaleMaster数据集和基线结果，我们旨在为未来研究建立基础，推动开发具有尺度一致性和可靠性的视觉SLAM系统。
英文摘要：
Recent advances in deep monocular visual Simultaneous Localization and Mapping (SLAM) have achieved impressive accuracy and dense reconstruction capabilities, yet their robustness to scale inconsistency in large-scale indoor environments remains largely unexplored. Existing benchmarks are limited to room-scale or structurally simple settings, leaving critical issues of intra-session scale drift and inter-session scale ambiguity insufficiently addressed. To fill this gap, we introduce the ScaleMaster Dataset, the first benchmark explicitly designed to evaluate scale consistency under challenging scenarios such as multi-floor structures, long trajectories, repetitive views, and low-texture regions. We systematically analyze the vulnerability of state-of-the-art deep monocular visual SLAM systems to scale inconsistency, providing both quantitative and qualitative evaluations. Crucially, our analysis extends beyond traditional trajectory metrics to include a direct map-to-map quality assessment using metrics like Chamfer distance against high-fidelity 3D ground truth. Our results reveal that while recent deep monocular visual SLAM systems demonstrate strong performance on existing benchmarks, they suffer from severe scale-related failures in realistic, large-scale indoor environments. By releasing the ScaleMaster dataset and baseline results, we aim to establish a foundation for future research toward developing scale-consistent and reliable visual SLAM systems.
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