Reconstructing 3D objects from a single image is an intriguing but
challenging problem. One promising solution is to utilize multi-view (MV) 3D
reconstruction to fuse generated MV images into consistent 3D objects. However,
the generated images usually suffer from inconsistent lighting, misaligned
geometry, and sparse views, leading to poor reconstruction quality. To cope
with these problems, we present a novel 3D reconstruction framework that
leverages intrinsic decomposition guidance, transient-mono prior guidance, and
view augmentation to cope with the three issues, respectively. Specifically, we
first leverage to decouple the shading information from the generated images to
reduce the impact of inconsistent lighting; then, we introduce mono prior with
view-dependent transient encoding to enhance the reconstructed normal; and
finally, we design a view augmentation fusion strategy that minimizes
pixel-level loss in generated sparse views and semantic loss in augmented
random views, resulting in view-consistent geometry and detailed textures. Our
approach, therefore, enables the integration of a pre-trained MV image
generator and a neural network-based volumetric signed distance function (SDF)
representation for a single image to 3D object reconstruction. We evaluate our
framework on various datasets and demonstrate its superior performance in both
quantitative and qualitative assessments, signifying a significant advancement
in 3D object reconstruction. Compared with the latest state-of-the-art method
Syncdreamer~\cite{liu2023syncdreamer}, we reduce the Chamfer Distance error by
about 36\% and improve PSNR by about 30\% .

通过利用内在分解指导、瞬态 - 单模先验指导和视图增强来解决光照不一致、几何不对齐和视图稀疏等问题，我们提出了一种新的 3D 重构框架，该框架能够将多视图图像生成与神经网络基于体积有符号距离函数的单一图像到 3D 对象重建相结合。在各种数据集上评估我们的方法，并在定量和定性评估中展示了其卓越性能，从而在 3D 物体重建方面取得了显著的进展。与最新的最先进方法 Syncdreamer 相比，我们将 Chamfer 距离误差降低了约 36％，将 PSNR 提高了约 30％。