GS-ASM：基于2D高斯泼溅监督的主动立体匹配
GS-ASM: 2DGS-Supervised Active Stereo Matching
利用2D高斯泼溅显式建模场景几何并合成主动投影图案，生成视差代理标签，从而将自监督主动立体匹配提升到监督训练水平；其混合监督正则化动态调整损失权重，可复用于其他多监督融合的深度估计任务。
原文依据 we develop an active 2D Gaussian Splatting (2DGS)-based synthesis method that explicitly models the scene geometry and the projected active pattern
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论由于缺乏真实标注，现有的主动立体匹配方法通常采用全自监督学习来生成精确的深度估计。尽管这些方法能够取得有希望的结果，但其性能与监督模型相比仍有明显差距。为填补这一差距，我们提出了一种新颖的框架，能够合成代理标签，从而在无需任何真实深度的情况下实现对深度主动立体网络的监督训练。为扩展训练数据并生成视差代理标签，我们开发了一种基于主动2D高斯泼溅（2DGS）的合成方法，该方法显式地建模场景几何与投影的主动图案。此外，为平衡训练过程中不同监督信号的变化贡献，我们设计了一种混合监督正则化策略，动态调整损失权重以实现稳定优化。我们还贡献了一个由手持RealSense相机采集的真实世界数据集，以及我们的主动2DGS模型，这将促进未来主动立体匹配的研究。大量的定性与定量实验表明，我们的方法在主动立体匹配任务上达到了最优性能。代码与数据集将公开释放。
英文原文摘要 1271 字符
Due to the lack of ground truth, existing methods of active stereo matching generally employ fully self-supervised learning to produce precise depth estimates. Although they can achieve promising results, their performance still has a noticeable gap compared with supervised models. To fill this gap, we propose a novel framework that synthesizes proxy labels to enable supervised training of deep active stereo networks without requiring any ground-truth depth. To expand the training data and generate disparity proxy labels, we develop an active 2D Gaussian Splatting (2DGS)-based synthesis method that explicitly models the scene geometry and the projected active pattern. Furthermore, to balance the varying contributions of different supervisions during training, we design a hybrid supervision regularization strategy that dynamically adjusts the loss weights to achieve stable optimization. We also contribute a real-world dataset captured by a handheld RealSense camera, along with our active 2DGS model, which facilitates future research on active stereo matching. Extensive qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art performance on active stereo matching task. The code and dataset will be publicly released.