Point cloud upsampling (PCU) enriches the representation of raw point clouds,
significantly improving the performance in downstream tasks such as
classification and reconstruction. Most of the existing point cloud upsampling
methods focus on sparse point cloud feature extraction and upsampling module
design. In a different way, we dive deeper into directly modelling the gradient
of data distribution from dense point clouds. In this paper, we proposed a
conditional denoising diffusion probability model (DDPM) for point cloud
upsampling, called PUDM. Specifically, PUDM treats the sparse point cloud as a
condition, and iteratively learns the transformation relationship between the
dense point cloud and the noise. Simultaneously, PUDM aligns with a dual
mapping paradigm to further improve the discernment of point features. In this
context, PUDM enables learning complex geometry details in the ground truth
through the dominant features, while avoiding an additional upsampling module
design. Furthermore, to generate high-quality arbitrary-scale point clouds
during inference, PUDM exploits the prior knowledge of the scale between sparse
point clouds and dense point clouds during training by parameterizing a rate
factor. Moreover, PUDM exhibits strong noise robustness in experimental
results. In the quantitative and qualitative evaluations on PU1K and PUGAN,
PUDM significantly outperformed existing methods in terms of Chamfer Distance
(CD) and Hausdorff Distance (HD), achieving state of the art (SOTA)
performance.

本研究提出了一种基于条件去噪扩散概率模型（DDPM）的点云上采样方法，称为 PUDM。PUDM 通过对稀疏点云进行条件建模，学习了稠密点云和噪声之间的转换关系，并利用双映射范式提高点特征的辨别力。此外，PUDM 还通过参数化速率因子，在训练过程中利用稀疏点云和稠密点云之间的比例关系生成高质量的任意尺度点云，实验结果表明其具有较强的噪声鲁棒性，相比现有方法，在 Chamfer 距离和 Hausdorff 距离上取得了最先进的性能。

一种基于条件去噪扩散概率模型的点云上采样方法

A Conditional Denoising Diffusion Probabilistic Model for Point Cloud  Upsampling

Reconstructing the 3D shape of an object from a single RGB image is a
long-standing and highly challenging problem in computer vision. In this paper,
we propose a novel method for single-image 3D reconstruction which generates a
sparse point cloud via a conditional denoising diffusion process. Our method
takes as input a single RGB image along with its camera pose and gradually
denoises a set of 3D points, whose positions are initially sampled randomly
from a three-dimensional Gaussian distribution, into the shape of an object.
The key to our method is a geometrically-consistent conditioning process which
we call projection conditioning: at each step in the diffusion process, we
project local image features onto the partially-denoised point cloud from the
given camera pose. This projection conditioning process enables us to generate
high-resolution sparse geometries that are well-aligned with the input image,
and can additionally be used to predict point colors after shape
reconstruction. Moreover, due to the probabilistic nature of the diffusion
process, our method is naturally capable of generating multiple different
shapes consistent with a single input image. In contrast to prior work, our
approach not only performs well on synthetic benchmarks, but also gives large
qualitative improvements on complex real-world data.

本文提出了一种新颖的基于条件去噪扩散过程的单张 RGB 图像三维重建方法，通过其几何一致的投影条件处理过程，能够生成与输入图像高度对齐的高分辨率稀疏几何形状，并自然地生成与单个输入图像一致的多个形状，相比之前的工作获得了更好的性能提升和质量改善。

$PC^2$: 基于投影条件的点云扩散用于单张图像的三维重建

$PC^2$: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction

We propose a deep neural network architecture to infer dense depth from an
image and a sparse point cloud. It is trained using a video stream and
corresponding synchronized sparse point cloud, as obtained from a LIDAR or
other range sensor, along with the intrinsic calibration parameters of the
camera. At inference time, the calibration of the camera, which can be
different than the one used for training, is fed as an input to the network
along with the sparse point cloud and a single image. A Calibrated
Backprojection Layer backprojects each pixel in the image to three-dimensional
space using the calibration matrix and a depth feature descriptor. The
resulting 3D positional encoding is concatenated with the image descriptor and
the previous layer output to yield the input to the next layer of the encoder.
A decoder, exploiting skip-connections, produces a dense depth map. The
resulting Calibrated Backprojection Network, or KBNet, is trained without
supervision by minimizing the photometric reprojection error. KBNet imputes
missing depth value based on the training set, rather than on generic
regularization. We test KBNet on public depth completion benchmarks, where it
outperforms the state of the art by 30.5% indoor and 8.8% outdoor when the same
camera is used for training and testing. When the test camera is different, the
improvement reaches 62%. Code available at:
this https URL

本研究使用卷积神经网络，引入校准回投影层来实现从图像和稀疏点云中推断密集深度图像，并通过最小化光度再投影误差来训练该神经网络，实验表明该网络在缺失值插值上比传统方法和目前状态的深度估计算法表现要好。