To prevent the mischievous use of synthetic (fake) point clouds produced by
generative models, we pioneer the study of detecting point cloud authenticity
and attributing them to their sources. We propose an attribution framework,
FAKEPCD, to attribute (fake) point clouds to their respective generative models
(or real-world collections). The main idea of FAKEPCD is to train an
attribution model that learns the point cloud features from different sources
and further differentiates these sources using an attribution signal. Depending
on the characteristics of the training point clouds, namely, sources and
shapes, we formulate four attribution scenarios: close-world, open-world,
single-shape, and multiple-shape, and evaluate FAKEPCD's performance in each
scenario. Extensive experimental results demonstrate the effectiveness of
FAKEPCD on source attribution across different scenarios. Take the open-world
attribution as an example, FAKEPCD attributes point clouds to known sources
with an accuracy of 0.82-0.98 and to unknown sources with an accuracy of
0.73-1.00. Additionally, we introduce an approach to visualize unique patterns
(fingerprints) in point clouds associated with each source. This explains how
FAKEPCD recognizes point clouds from various sources by focusing on distinct
areas within them. Overall, we hope our study establishes a baseline for the
source attribution of (fake) point clouds.

为了防止生成模型产生的合成（伪造）点云被恶意使用，我们首次提出检测点云真实性并将其归属于其来源的研究，通过引入 FAKEPCD，一个将（伪造）点云归属于其生成模型（或真实世界集合）的归属框架。FAKEPCD 的主要思想是训练一个归属模型，该模型从不同来源中学习点云特征并通过归属信号区分这些来源。根据训练点云的特征，即来源和形状，我们制定了四种归属场景：封闭世界，开放世界，单一形状和多形状，并在每个场景中评估了 FAKEPCD 的性能。广泛的实验结果表明 FAKEPCD 在不同场景下对来源归属的有效性。以开放世界归属为例，FAKEPCD 可以将点云归属于已知来源的准确率为 0.82-0.98，将点云归属于未知来源的准确率为 0.73-1.00。此外，我们介绍了一种可视化点云中与每个来源相关的独特模式（指纹）的方法。这解释了 FAKEPCD 如何通过关注其中的独特区域来识别来自各种来源的点云。总体而言，我们希望我们的研究为（伪造）点云的来源归属奠定基础。