In this paper, we introduce Era3D, a novel multiview diffusion method that
generates high-resolution multiview images from a single-view image. Despite
significant advancements in multiview generation, existing methods still suffer
from camera prior mismatch, inefficacy, and low resolution, resulting in
poor-quality multiview images. Specifically, these methods assume that the
input images should comply with a predefined camera type, e.g. a perspective
camera with a fixed focal length, leading to distorted shapes when the
assumption fails. Moreover, the full-image or dense multiview attention they
employ leads to an exponential explosion of computational complexity as image
resolution increases, resulting in prohibitively expensive training costs. To
bridge the gap between assumption and reality, Era3D first proposes a
diffusion-based camera prediction module to estimate the focal length and
elevation of the input image, which allows our method to generate images
without shape distortions. Furthermore, a simple but efficient attention layer,
named row-wise attention, is used to enforce epipolar priors in the multiview
diffusion, facilitating efficient cross-view information fusion. Consequently,
compared with state-of-the-art methods, Era3D generates high-quality multiview
images with up to a 512*512 resolution while reducing computation complexity by
12x times. Comprehensive experiments demonstrate that Era3D can reconstruct
high-quality and detailed 3D meshes from diverse single-view input images,
significantly outperforming baseline multiview diffusion methods.

Era3D 是一种多视角扩散方法，通过单视角图像生成高分辨率的多视角图像。它提出了扩散式的摄像机预测模块以准确估计输入图像的焦距和仰角，使用行向注意力层来加强多视角扩散中的极线先验，实现高质量的多视角图像生成和具体的 3D 网格重建。

Era3D：高分辨率多视角扩散利用高效分行注意力

Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise  Attention

As a promising 3D generation technique, multiview diffusion (MVD) has
received a lot of attention due to its advantages in terms of generalizability,
quality, and efficiency. By finetuning pretrained large image diffusion models
with 3D data, the MVD methods first generate multiple views of a 3D object
based on an image or text prompt and then reconstruct 3D shapes with multiview
3D reconstruction. However, the sparse views and inconsistent details in the
generated images make 3D reconstruction challenging. We present MVD$^2$, an
efficient 3D reconstruction method for multiview diffusion (MVD) images.
MVD$^2$ aggregates image features into a 3D feature volume by projection and
convolution and then decodes volumetric features into a 3D mesh. We train
MVD$^2$ with 3D shape collections and MVD images prompted by rendered views of
3D shapes. To address the discrepancy between the generated multiview images
and ground-truth views of the 3D shapes, we design a simple-yet-efficient
view-dependent training scheme. MVD$^2$ improves the 3D generation quality of
MVD and is fast and robust to various MVD methods. After training, it can
efficiently decode 3D meshes from multiview images within one second. We train
MVD$^2$ with Zero-123++ and ObjectVerse-LVIS 3D dataset and demonstrate its
superior performance in generating 3D models from multiview images generated by
different MVD methods, using both synthetic and real images as prompts.

MVD$^2$ 是一种有效的三维重建方法，通过聚合图像特征并将其解码成三维网格，改善了多视角扩散生成图像的三维生成质量，且在不同的多视角扩散方法下具有快速和稳健的性能。