Title: TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update Show Chinese title

Title: TRAN-D：基于2D高斯点云的稀疏视图透明物体深度重建方法，通过物理模拟实现场景更新

Abstract: Understanding the 3D geometry of transparent objects from RGB images is challenging due to their inherent physical properties, such as reflection and refraction. To address these difficulties, especially in scenarios with sparse views and dynamic environments, we introduce TRAN-D, a novel 2D Gaussian Splatting-based depth reconstruction method for transparent objects. Our key insight lies in separating transparent objects from the background, enabling focused optimization of Gaussians corresponding to the object. We mitigate artifacts with an object-aware loss that places Gaussians in obscured regions, ensuring coverage of invisible surfaces while reducing overfitting. Furthermore, we incorporate a physics-based simulation that refines the reconstruction in just a few seconds, effectively handling object removal and chain-reaction movement of remaining objects without the need for rescanning. TRAN-D is evaluated on both synthetic and real-world sequences, and it consistently demonstrated robust improvements over existing GS-based state-of-the-art methods. In comparison with baselines, TRAN-D reduces the mean absolute error by over 39% for the synthetic TRansPose sequences. Furthermore, despite being updated using only one image, TRAN-D reaches a {\delta} < 2.5 cm accuracy of 48.46%, over 1.5 times that of baselines, which uses six images. Code and more results are available at https://jeongyun0609.github.io/TRAN-D/. Show Chinese abstract

Abstract: 理解透明物体的3D几何结构从RGB图像中是具有挑战性的，这是由于它们固有的物理特性，如反射和折射。 为了解决这些困难，特别是在视图稀疏和动态环境的情况下，我们引入了TRAN-D，一种基于2D高斯点云的深度重建方法，用于透明物体。 我们的关键见解在于将透明物体与背景分离，从而专注于优化对应于物体的高斯分布。 我们通过一个对象感知损失来减轻伪影，将高斯分布在被遮挡区域，确保覆盖不可见表面的同时减少过拟合。 此外，我们引入了一个基于物理的模拟，在几秒钟内精炼重建，有效地处理物体移除和剩余物体的连锁运动，而无需重新扫描。 TRAN-D在合成和现实世界序列上进行了评估，并且始终表现出对现有基于GS的最先进方法的鲁棒改进。 与基线相比，TRAN-D在合成TRansPose序列上的平均绝对误差减少了超过39%。 此外，尽管仅使用一张图像进行更新，TRAN-D达到了{\delta } < 2.5 cm的准确率48.46%，是使用六张图像的基线的1.5倍以上。 代码和更多结果可在https://jeongyun0609.github.io/TRAN-D/获取。