Text-to-3D generation by distilling pretrained large-scale text-to-image
diffusion models has shown great promise but still suffers from inconsistent 3D
geometric structures (Janus problems) and severe artifacts. The aforementioned
problems mainly stem from 2D diffusion models lacking 3D awareness during the
lifting. In this work, we present GeoDream, a novel method that incorporates
explicit generalized 3D priors with 2D diffusion priors to enhance the
capability of obtaining unambiguous 3D consistent geometric structures without
sacrificing diversity or fidelity. Specifically, we first utilize a multi-view
diffusion model to generate posed images and then construct cost volume from
the predicted image, which serves as native 3D geometric priors, ensuring
spatial consistency in 3D space. Subsequently, we further propose to harness 3D
geometric priors to unlock the great potential of 3D awareness in 2D diffusion
priors via a disentangled design. Notably, disentangling 2D and 3D priors
allows us to refine 3D geometric priors further. We justify that the refined 3D
geometric priors aid in the 3D-aware capability of 2D diffusion priors, which
in turn provides superior guidance for the refinement of 3D geometric priors.
Our numerical and visual comparisons demonstrate that GeoDream generates more
3D consistent textured meshes with high-resolution realistic renderings (i.e.,
1024 $\times$ 1024) and adheres more closely to semantic coherence.

通过结合三维几何先验和二维扩散先验，GeoDream 方法能够生成具有一致的三维几何结构的文本到三维模型，并提供更高分辨率、真实感的渲染和更好的语义一致性。

GeoDream: 高保真和一致的 3D 生成中的解耦 2D 和几何先验

GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and  Consistent 3D Generation

Point clouds captured in real-world applications are often incomplete due to
the limited sensor resolution, single viewpoint, and occlusion. Therefore,
recovering the complete point clouds from partial ones becomes an indispensable
task in many practical applications. In this paper, we present a new method
that reformulates point cloud completion as a set-to-set translation problem
and design a new model, called PoinTr that adopts a transformer encoder-decoder
architecture for point cloud completion. By representing the point cloud as a
set of unordered groups of points with position embeddings, we convert the
point cloud to a sequence of point proxies and employ the transformers for
point cloud generation. To facilitate transformers to better leverage the
inductive bias about 3D geometric structures of point clouds, we further devise
a geometry-aware block that models the local geometric relationships
explicitly. The migration of transformers enables our model to better learn
structural knowledge and preserve detailed information for point cloud
completion. Furthermore, we propose two more challenging benchmarks with more
diverse incomplete point clouds that can better reflect the real-world
scenarios to promote future research. Experimental results show that our method
outperforms state-of-the-art methods by a large margin on both the new
benchmarks and the existing ones. Code is available at
this https URL

该论文提出了一种新方法 PoinTr，将点云补全问题重新构造为集合对集合翻译问题，并采用 Transformer 编码器 - 解码器架构。通过使用定位嵌入在无序点组中表示点云，我们将点云转换为点代理序列，并使用变形器进行点云生成。在点云的局部几何关系中，我们进一步设计了一种几何感知块以更好地利用点云的 3D 几何结构归纳偏差，使得变形器能够更好地学习结构知识和保留详细信息，从而完善点云。此外，我们提出了两个具有更多不完整点云的具有挑战性的基准测试，以更好地反映现实世界的情况。实证结果表明，我们的方法在新的基准测试和现有基准测试上均优于现有最先进的方法。