Abstract
Detecting and segmenting moving objects from a moving monocular camera is challenging in the presence of unknown camera motion, diverse object motions and complex scene structures. Most existing methods rely on a single motion cue to perform motion segmentation, which is usually insufficient when facing different complex environments. While a few recent deep learning based methods are able to combine multiple motion cues to achieve improved accuracy, they depend heavily on vast datasets and extensive annotations, making them less adaptable to new scenarios. To address these limitations, we propose a novel monocular dense segmentation method that achieves state-of-the-art motion segmentation results in a zero-shot manner. The proposed method synergestically combines the strengths of deep learning and geometric model fusion methods by performing geometric model fusion on object proposals. Experiments show that our method achieves competitive results on several motion segmentation datasets and even surpasses some state-of-the-art supervised methods on certain benchmarks, while not being trained on any data. We also present an ablation study to show the effectiveness of combining different geometric models together for motion segmentation, highlighting the value of our geometric model fusion strategy.
Abstract (translated)
在未知相机运动、多样物体运动和复杂场景结构的背景下，检测和分割运动物体是一个具有挑战性的任务。大多数现有方法仅依赖于单个运动提示进行运动分割，这通常不足以处理不同的复杂环境。虽然一些基于深度学习的最新方法能够结合多个运动提示以实现更准确的运动分割，但它们依赖于大量数据和广泛的注释，因此对新的场景不够适应。为了克服这些限制，我们提出了一种新颖的单目相机密集分割方法，在零散拍摄的情况下实现最先进的运动分割。该方法通过在物体提议上进行几何模型融合，将深度学习方法和几何模型融合方法的优势相结合。实验证明，我们的方法在多个运动分割数据集上取得了竞争性的结果，甚至在某些基准上超过了某些最先进的监督方法。此外，我们还进行了消融研究，以展示将不同几何模型结合进行运动分割的有效性，并强调了我们几何模型融合策略的价值。
URL
https://arxiv.org/abs/2405.01723
