利用预训练深度基础模型提升全向立体匹配性能
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
AI总结：
针对现有全向立体匹配方法因真实数据稀缺导致精度有限的问题，提出DFI-OmniStereo方法，结合预训练单目深度基础模型与两阶段训练策略，在Helvipad数据集上实现SOTA，视差MAE较此前最优方法降低约16%。
摘要：
全向深度感知对于需要在360°全视场范围内实现场景理解的移动机器人应用至关重要。基于相机的配置方案通过立体深度估计生成密集、高分辨率的深度图，无需依赖昂贵的主动传感设备，是一种高性价比的选择。然而，由于真实世界数据稀缺，现有的全向立体匹配方法在不同环境、深度范围和光照条件下的深度精度十分有限。我们提出DFI-OmniStereo，这是一种新型全向立体匹配方法，在基于迭代优化的立体匹配架构中，利用大规模预训练基础模型进行相对单目深度估计。我们提出了专门的两阶段训练策略，在尺度不变微调之前，将相对单目深度特征用于全向立体匹配任务。DFI-OmniStereo在真实世界Helvipad数据集上取得了当前最优结果，与此前最优的全向立体方法相比，视差平均绝对误差（MAE）降低了约16%。
英文摘要：
Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-effective option by using stereo depth estimation to generate dense, high-resolution depth maps without relying on expensive active sensing. However, existing omnidirectional stereo matching approaches achieve only limited depth accuracy across diverse environments, depth ranges, and lighting conditions, due to the scarcity of real-world data. We present DFI-OmniStereo, a novel omnidirectional stereo matching method that leverages a large-scale pre-trained foundation model for relative monocular depth estimation within an iterative optimization-based stereo matching architecture. We introduce a dedicated two-stage training strategy to utilize the relative monocular depth features for our omnidirectional stereo matching before scale-invariant fine-tuning. DFI-OmniStereo achieves state-of-the-art results on the real-world Helvipad dataset, reducing disparity MAE by approximately 16% compared to the previous best omnidirectional stereo method.
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