We propose the use of a Transformer to accurately predict normals from point
clouds with noise and density variations. Previous learning-based methods
utilize PointNet variants to explicitly extract multi-scale features at
different input scales, then focus on a surface fitting method by which local
point cloud neighborhoods are fitted to a geometric surface approximated by
either a polynomial function or a multi-layer perceptron (MLP). However,
fitting surfaces to fixed-order polynomial functions can suffer from
overfitting or underfitting, and learning MLP-represented hyper-surfaces
requires pre-generated per-point weights. To avoid these limitations, we first
unify the design choices in previous works and then propose a simplified
Transformer-based model to extract richer and more robust geometric features
for the surface normal estimation task. Through extensive experiments, we
demonstrate that our Transformer-based method achieves state-of-the-art
performance on both the synthetic shape dataset PCPNet, and the real-world
indoor scene dataset SceneNN, exhibiting more noise-resilient behavior and
significantly faster inference. Most importantly, we demonstrate that the
sophisticated hand-designed modules in existing works are not necessary to
excel at the task of surface normal estimation.

使用 Transformer 从带有噪声和密度变化的点云中准确预测法线，在合成形状数据集 PCPNet 和真实世界室内场景数据集 SceneNN 上实现了最先进的性能，表现出更强的抗噪性和显著更快的推理速度，同时证明了现有工作中的复杂手工设计模块在表面法线估计任务中并非必需。

基于 Transformer 的表面法向量估计

Surface Normal Estimation with Transformers

Generating realistic 3D faces is of high importance for computer graphics and
computer vision applications. Generally, research on 3D face generation
revolves around linear statistical models of the facial surface. Nevertheless,
these models cannot represent faithfully either the facial texture or the
normals of the face, which are very crucial for photo-realistic face synthesis.
Recently, it was demonstrated that Generative Adversarial Networks (GANs) can
be used for generating high-quality textures of faces. Nevertheless, the
generation process either omits the geometry and normals, or independent
processes are used to produce 3D shape information. In this paper, we present
the first methodology that generates high-quality texture, shape, and normals
jointly, which can be used for photo-realistic synthesis. To do so, we propose
a novel GAN that can generate data from different modalities while exploiting
their correlations. Furthermore, we demonstrate how we can condition the
generation on the expression and create faces with various facial expressions.
The qualitative results shown in this paper are compressed due to size
limitations, full-resolution results and the accompanying video can be found in
the supplementary documents. The code and models are available at the project
page: this https URL

本研究提出了一种新颖的 GAN 方法，可以联合生成面部纹理、形状和法向量信息，用于进行类真实面部合成。同时，我们展示了如何以面部表情为条件生成具有不同表情的面部。

通过 Trunk-Branch 生成对抗网络合成耦合的三维人脸模态

Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative  Adversarial Networks

Recent years have seen the development of mature solutions for reconstructing
deformable surfaces from a single image, provided that they are relatively
well-textured. By contrast, recovering the 3D shape of texture-less surfaces
remains an open problem, and essentially relates to Shape-from-Shading. In this
paper, we introduce a data-driven approach to this problem. We introduce a
general framework that can predict diverse 3D representations, such as meshes,
normals, and depth maps. Our experiments show that meshes are ill-suited to
handle texture-less 3D reconstruction in our context. Furthermore, we
demonstrate that our approach generalizes well to unseen objects, and that it
yields higher-quality reconstructions than a state-of-the-art SfS technique,
particularly in terms of normal estimates. Our reconstructions accurately model
the fine details of the surfaces, such as the creases of a T-Shirt worn by a
person.

论文提出了一种基于数据驱动的方法，可以用于从单张图片中预测纹理缺失表面的三维重建结果，结果可以表示为网格、法线或深度图等形式，研究表明在文中所述情境下，网格并不适用于处理纹理缺失表面的三维重建。该方法可以很好地泛化到未见过的物体，并且更优于目前最先进的 SfS 技术。