多视角金字塔变换器：看得更粗以看得更广
Multi-view Pyramid Transformer: Look Coarser to See Broader
AI总结：
提出MVP，一种可扩展的多视角变换器架构，通过局部到全局的视角层次和细到粗的空间层次，实现从数十到数百张图像中单次前向重建大型3D场景，结合3D高斯泼溅达到最先进的可泛化重建质量。
摘要：
我们提出了多视角金字塔变换器（MVP），这是一种可扩展的多视角变换器架构，能够直接从数十到数百张图像中单次前向重建大型3D场景。借鉴“看得更广以见全貌，看得更细以见细节”的思想，MVP建立在两个核心设计原则之上：1）局部到全局的视角间层次，逐渐将模型的视角从局部视图扩展到组，最终到整个场景；2）细到粗的视角内层次，从详细的空间表示开始，逐步聚合为紧凑、信息密集的令牌。这种双重层次结构实现了计算效率和表示丰富性，使得快速重建大型复杂场景成为可能。我们在多个数据集上验证了MVP，并表明当与3D高斯泼溅作为底层3D表示结合时，它在广泛视角配置下实现了最先进的可泛化重建质量，同时保持高效率和可扩展性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
