跨实例高斯泼溅配准：基于几何感知特征引导的对齐
Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment
提出一种基于视角引导球面图特征和迭代绝对定向求解器的3DGS配准框架，可处理不同物体间的相似变换估计，为3DGS模型对齐和类别级配准提供了可复用技术。
原文依据 GSA leverages viewpoint-guided spherical map features to obtain robust correspondences and introduces a two-step optimization framework
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出高斯泼溅对齐（GSA），一种通过相似变换（旋转、平移和缩放）对齐两个独立三维高斯泼溅（3DGS）模型的新方法，即使它们属于同一类别的不同物体（例如不同的汽车）。相比之下，现有方法只能对齐同一物体的3DGS模型（例如同一辆汽车），并且通常必须给定真实尺度作为输入，而我们成功估计了尺度。GSA利用视角引导的球面图特征获得鲁棒对应关系，并引入一个两步优化框架，在对齐3DGS模型的同时保持它们固定。首先，我们应用迭代特征引导的绝对定向求解器作为粗配准，该求解器对较差的初始化（例如180度错位或10倍尺度差距）具有鲁棒性。接着，我们使用一个精细配准步骤，该步骤强制多视图特征一致性，其灵感来自逆辐射场公式。第一步已经达到了最优性能，第二步进一步改善了结果。在同一物体的情况下，GSA优于先前工作，且通常优势明显，即使其他方法被给定真实尺度。在更困难的同一类别不同物体的情况下，GSA大幅超越它们，为类别级3DGS配准提供了首个有效解决方案，并解锁了新的应用。项目网页：https://bgu-cs-vil.github.io/GSA-project
英文原文摘要 1453 字符
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., 180deg 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