The paper introduces PointVGGT, a zero‑shot framework for multiview RGB‑D point cloud registration that replaces the traditional pairwise‑then‑global pipeline. It employs a foundation‑then‑refinement paradigm, using visual geometry foundation models to directly recover metrically consistent global poses and then refining them with voxelized spatial hashing and IRLS‑based bundle adjustment. Experiments on indoor, object‑centric, and outdoor datasets demonstrate superior registration accuracy and computational efficiency without any training.
By Haobo Jiang, Liang Yu, Jianmin Zheng
The paper introduces PointVGGT, a zero‑shot framework for multiview RGB‑D point cloud registration that avoids pairwise estimation by using a foundation‑then‑refinement paradigm. In the foundation stage, it recovers metrically consistent global poses by grounding scale‑ambiguous predictions from visual geometry foundation models against metric depth. The refinement stage employs voxelized spatial hashing and IRLS‑based bundle adjustment to efficiently refine poses, achieving high accuracy and computational efficiency across indoor, object‑centric, and outdoor datasets.
Indoor visual relocalization plays a critical role in emerging spatial and embodied AI applications. However, prior research was predominantly devoted to low-level vision schemes, struggling to perceive scene semantics and compositions, which limits both interpretability and applicability.
arXiv:2608.23290v1 Announce Type: new
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arXiv:2609.00713v1 Announce Type: new
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