The ability to predict future structure features of environments based on
past perception information is extremely needed by autonomous vehicles, which
helps to make the following decision-making and path planning more reasonable.
Recently, point cloud prediction (PCP) is utilized to predict and describe
future environmental structures by the point cloud form. In this letter, we
propose a novel efficient Transformer-based network to predict the future LiDAR
point clouds exploiting the past point cloud sequences. We also design a
semantic auxiliary training strategy to make the predicted LiDAR point cloud
sequence semantically similar to the ground truth and thus improves the
significance of the deployment for more tasks in real-vehicle applications. Our
approach is completely self-supervised, which means it does not require any
manual labeling and has a solid generalization ability toward different
environments. The experimental results show that our method outperforms the
state-of-the-art PCP methods on the prediction results and semantic similarity,
and has a good real-time performance. Our open-source code and pre-trained
models are available at this https URL

本文提出一种新的高效 Transformer-based 的点云预测网络，并设计了语义辅助训练策略来对未来的 LiDAR 点云进行预测，该方法是完全自监督的，并且在预测结果和语义相似性上表现出超越最先进的 PCP 方法的性能，具有良好的实时性能。

PCPNet：一种用于点云预测的高效语义增强变换网络

PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point  Cloud Prediction

Exploiting past 3D LiDAR scans to predict future point clouds is a promising
method for autonomous mobile systems to realize foresighted state estimation,
collision avoidance, and planning. In this paper, we address the problem of
predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
Estimating the future scene on the sensor level does not require any preceding
steps as in localization or tracking systems and can be trained
self-supervised. We propose an end-to-end approach that exploits a 2D range
image representation of each 3D LiDAR scan and concatenates a sequence of range
images to obtain a 3D tensor. Based on such tensors, we develop an
encoder-decoder architecture using 3D convolutions to jointly aggregate spatial
and temporal information of the scene and to predict the future 3D point
clouds. We evaluate our method on multiple datasets and the experimental
results suggest that our method outperforms existing point cloud prediction
architectures and generalizes well to new, unseen environments without
additional fine-tuning. Our method operates online and is faster than the
common LiDAR frame rate of 10 Hz.

本文提出了一种使用 3D LiDAR 扫描来预测未来点云的方法，该方法可用于实现自主移动系统的预测状态估计、避碰和规划，通过使用 2D 范围图像表示扫描数据并连接一系列范围图像来实现端到端的预测，最终通过 3D 卷积来预测未来的 3D 点云，并通过多个数据集进行实验验证，结果表明本文提出的方法比现有的点云预测结构更优且能够很好地适用于新的未知环境。

使用 3D 时空卷积网络进行自监督点云预测

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal  Convolutional Networks

Predicting the future can significantly improve the safety of intelligent
vehicles, which is a key component in autonomous driving. 3D point clouds
accurately model 3D information of surrounding environment and are crucial for
intelligent vehicles to perceive the scene. Therefore, prediction of 3D point
clouds has great significance for intelligent vehicles, which can be utilized
for numerous further applications. However, due to point clouds are unordered
and unstructured, point cloud prediction is challenging and has not been deeply
explored in current literature. In this paper, we propose a novel motion-based
neural network named MoNet. The key idea of the proposed MoNet is to integrate
motion features between two consecutive point clouds into the prediction
pipeline. The introduction of motion features enables the model to more
accurately capture the variations of motion information across frames and thus
make better predictions for future motion. In addition, content features are
introduced to model the spatial content of individual point clouds. A recurrent
neural network named MotionRNN is proposed to capture the temporal correlations
of both features. Besides, we propose an attention-based motion align module to
address the problem of missing motion features in the inference pipeline.
Extensive experiments on two large scale outdoor LiDAR datasets demonstrate the
performance of the proposed MoNet. Moreover, we perform experiments on
applications using the predicted point clouds and the results indicate the
great application potential of the proposed method.

本文提出了一种名为 MoNet 的基于运动的神经网络，它将运动特征和内容特征结合起来用于预测未来的点云，以提高自动驾驶中智能汽车对其周围环境的感知和安全性。实验结果表明，该方法具有重要的应用前景。