Abstract
Occlusion Boundary Estimation (OBE) identifies boundaries arising from both inter-object occlusions and self-occlusion within individual objects, distinguishing intrinsic object edges from occlusion-induced contours to improve scene understanding and 3D reconstruction capacity. This is closely related to Monocular Depth Estimation (MDE), which infers depth from a single image, as occlusion boundaries provide critical geometric cues for resolving depth ambiguities, while depth priors can conversely refine occlusion reasoning in complex scenes. In this paper, we propose a novel network, MoDOT, that first jointly estimates depth and OBs. We propose CASM, a cross-attention multi-scale strip convolution module, leverages mid-level OB features to significantly enhance depth prediction. Additionally, we introduce an occlusion-aware loss function, OBDCL, which encourages sharper and more accurate depth boundaries. Extensive experiments on both real and synthetic datasets demonstrate the mutual benefits of jointly estimating depth and OB, and highlight the effectiveness of our model design. Our method achieves the state-of-the-art (SOTA) on both our proposed synthetic datasets and one popular real dataset, NYUD-v2, significantly outperforming multi-task baselines. Besides, without domain adaptation, results on real-world depth transfer are comparable to the competitors, while preserving sharp occlusion boundaries for geometric fidelity. We will release our code, pre-trained models, and datasets to support future research in this direction.
Abstract (translated)
遮挡边界估计（OBE）旨在识别由物体间遮挡和单个对象内部自我遮挡产生的边界，区分固有的对象边缘与遮挡引起的轮廓，以此来提高场景理解和三维重建的能力。这与单目深度估计（MDE）密切相关，后者通过单一图像推断深度。遮挡边界的几何线索对于解决深度模糊性至关重要，而深度先验信息则可以反过来在复杂场景中改进遮挡推理。 本文提出了一种新的网络模型MoDOT，该模型首先联合估计深度和遮挡边界（OB）。我们提出了一个跨注意力多尺度带状卷积模块CASM，利用中级的OB特征显著提高深度预测能力。此外，还引入了一个感知遮挡的损失函数OBDCL，以鼓励更尖锐、更准确的深度边界。 在真实数据集和合成数据集上的广泛实验表明了联合估计深度和OB之间的相互利益，并突显了我们模型设计的有效性。我们的方法在我们提出的合成数据集中以及流行的NYUD-v2现实数据集中达到了最先进的性能（SOTA），显著超越了多任务基线。此外，无需领域适应的情况下，在实际世界的深度转移结果可以与竞争对手相媲美，同时保持清晰的遮挡边界以确保几何精度。 我们将发布我们的代码、预训练模型和数据集，以支持未来在此方向上的研究工作。
URL
https://arxiv.org/abs/2505.21231
