跨实例3D高斯点云配准，无需真实尺度也能精准对齐同类不同物体。
Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment
- 用视角引导的球面特征匹配对应点，实现鲁棒配准。
- 两步优化框架：粗配准抗初始化误差，精配准保证多视角一致性。
- 首次解决同类别不同物体的3DGS配准问题，适合新场景重建应用。
我们提出高斯点云对齐（GSA），一种通过相似变换（旋转、平移、缩放）对齐两个独立3D高斯点云（3DGS）模型的新方法，即使它们属于同一类别的不同物体（如不同汽车）。现有方法仅能对齐同一物体的3DGS模型，且常需给定真实尺度，而我们能成功估计尺度。GSA利用视角引导的球面图特征获取鲁棒对应关系，并引入两步优化框架，在保持3DGS模型不变的前提下完成对齐。首先，采用迭代特征引导的绝对定向求解器进行粗配准，对不良初始化（如180度偏移或10倍尺度差异）具有鲁棒性；其次，使用基于逆辐射场思想的细配准步骤，强制多视角特征一致性。第一步即达到当前最优性能，第二步进一步提升效果。在相同物体情况下，GSA显著优于先前方法，即便后者已知真实尺度；在更难的同类不同物场景中，GSA大幅超越现有方法，首次提供有效的类别级3DGS配准解决方案，开启新应用可能。项目网页：https://bgu-cs-vil.github.io/GSA-project/
原文摘要 · Abstract (English)
We present Gaussian Splatting Alignment (GSA), a novel method for aligning two independent 3D Gaussian Splatting (3DGS) models via a similarity transformation (rotation, translation, and scale), even when they are of different objects in the same category (e.g., different cars). In contrast, existing methods can only align 3DGS models of the same object (e.g., the same car) and often must be given true scale as input, while we estimate it successfully. GSA leverages viewpoint-guided spherical map features to obtain robust correspondences and introduces a two-step optimization framework that aligns 3DGS models while keeping them fixed. First, we apply an iterative feature-guided absolute orientation solver as our coarse registration, which is robust to poor initialization (e.g., 180 degrees misalignment or a 10x scale gap). Next, we use a fine registration step that enforces multi-view feature consistency, inspired by inverse radiance-field formulations. The first step already achieves state-of-the-art performance, and the second further improves results. In the same-object case, GSA outperforms prior works, often by a large margin, even when the other methods are given the true scale. In the harder case of different objects in the same category, GSA vastly surpasses them, providing the first effective solution for category-level 3DGS registration and unlocking new applications. Project webpage: https://bgu-cs-vil.github.io/GSA-project/
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