Despite recent advancements in the Large Reconstruction Model (LRM)
demonstrating impressive results, when extending its input from single image to
multiple images, it exhibits inefficiencies, subpar geometric and texture
quality, as well as slower convergence speed than expected.
It is attributed to that, LRM formulates 3D reconstruction as a naive
images-to-3D translation problem, ignoring the strong 3D coherence among the
input images. In this paper, we propose a Multi-view Large Reconstruction Model
(M-LRM) designed to efficiently reconstruct high-quality 3D shapes from
multi-views in a 3D-aware manner. Specifically, we introduce a multi-view
consistent cross-attention scheme to enable M-LRM to accurately query
information from the input images. Moreover, we employ the 3D priors of the
input multi-view images to initialize the tri-plane tokens. Compared to LRM,
the proposed M-LRM can produce a tri-plane NeRF with $128 \times 128$
resolution and generate 3D shapes of high fidelity. Experimental studies
demonstrate that our model achieves a significant performance gain and faster
training convergence than LRM. Project page: this https URL

通过引入跨视图一致的交叉注意力机制和利用三维先验信息，提出了一种高效的多视角大型重建模型 (M-LRM)，能够从多视图中以三维感知的方式高质量地重建三维形状，并且相比于 LRM 能够生成分辨率为 $128 	imes 128$ 的三平面 NeRF，具有更高的保真度和训练收敛速度。