  Feed-forward 3D generative models like the Large Reconstruction Model (LRM)
have demonstrated exceptional generation speed. However, the transformer-based
methods do not leverage the geometric priors of the triplane component in their
architecture, often leading to sub-optimal quality given the limited size of 3D
data and slow training. In this work, we present the Convolutional
Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D
generative model. Recognizing the limitations posed by sparse 3D data, we
highlight the necessity of integrating geometric priors into network design.
CRM builds on the key observation that the visualization of triplane exhibits
spatial correspondence of six orthographic images. First, it generates six
orthographic view images from a single input image, then feeds these images
into a convolutional U-Net, leveraging its strong pixel-level alignment
capabilities and significant bandwidth to create a high-resolution triplane.
CRM further employs Flexicubes as geometric representation, facilitating direct
end-to-end optimization on textured meshes. Overall, our model delivers a
high-fidelity textured mesh from an image in just 10 seconds, without any
test-time optimization.
