Multi-view Pyramid Transformer: Look Coarser to See Broader
We propose Multi-view Pyramid Transformer (MVP), a scalable multi-view transformer architecture that directly reconstructs large 3D scenes from tens to hundreds of images in a single forward pass. Drawing on the idea of ``looking broader to see the whole, looking finer to see the details," MVP is built on two core design principles: 1) a local-to-global inter-view hierarchy that gradually broadens the model's perspective from local views to groups and ultimately the full scene, and 2) a fine-to-coarse intra-view hierarchy that starts from detailed spatial representations and progressively aggregates them into compact, information-dense tokens. This dual hierarchy achieves both computational efficiency and representational richness, enabling fast reconstruction of large and complex scenes. We validate MVP on diverse datasets and show that, when coupled with 3D Gaussian Splatting as the underlying 3D representation, it achieves state-of-the-art generalizable reconstruction quality while maintaining high efficiency and scalability across a wide range of view configurations.
6937ca7819d912300c34a468
Gyeongjin Kang
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2025-12-09T09:21:20.954Z
64aa93e799639cc312795ea8
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Gyeongjin Kang
Gynjn
user
Gynjn
6937ca7819d912300c34a469
Seungkwon Yang
6937ca7819d912300c34a46a
Seungtae Nam
6937ca7819d912300c34a46b
Younggeun Lee
6937ca7819d912300c34a46c
Jungwoo Kim
6937ca7819d912300c34a46d
Eunbyung Park
Multi-view Pyramid Transformer: Look Coarser to See Broader
We propose Multi-view Pyramid Transformer (MVP), a scalable multi-view transformer architecture that directly reconstructs large 3D scenes from tens to hundreds of images in a single forward pass. Drawing on the idea of ``looking broader to see the whole, looking finer to see the details," MVP is built on two core design principles: 1) a local-to-global inter-view hierarchy that gradually broadens the model's perspective from local views to groups and ultimately the full scene, and 2) a fine-to-coarse intra-view hierarchy that starts from detailed spatial representations and progressively aggregates them into compact, information-dense tokens. This dual hierarchy achieves both computational efficiency and representational richness, enabling fast reconstruction of large and complex scenes. We validate MVP on diverse datasets and show that, when coupled with 3D Gaussian Splatting as the underlying 3D representation, it achieves state-of-the-art generalizable reconstruction quality while maintaining high efficiency and scalability across a wide range of view configurations.
MVP, a scalable multi-view transformer architecture, efficiently reconstructs large 3D scenes from multiple images using dual hierarchies and achieves state-of-the-art quality.
Multi-view Pyramid Transformer MVP local-to-global inter-view hierarchy fine-to-coarse intra-view hierarchy 3D Gaussian Splatting 3D scenes generalizable reconstruction quality scalability view configurations