# Temporal LiDAR Frame Prediction for Autonomous Driving

arXiv: https://arxiv.org/abs/2012.09409 · HTML: https://mlsanity.com/paper/2012.09409/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2110.04076/) (2021)
- [LiDAR-based 4D Occupancy Completion and Forecasting](https://mlsanity.com/paper/2310.11239/) (2023)
- [PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction](https://mlsanity.com/paper/2304.07773/) (2023)
- [Sequential Point Clouds: A Survey](https://mlsanity.com/paper/2204.09337/) (2022)
- [Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence](https://mlsanity.com/paper/2312.08054/) (2023)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving](https://mlsanity.com/paper/2112.04401/) (2021)

## Abstract (arXiv)

Anticipating the future in a dynamic scene is critical for many fields such as autonomous driving and robotics. In this paper we propose a class of novel neural network architectures to predict future LiDAR frames given previous ones. Since the ground truth in this application is simply the next frame in the sequence, we can train our models in a self-supervised fashion. Our proposed architectures are based on FlowNet3D and Dynamic Graph CNN. We use Chamfer Distance (CD) and Earth Mover's Distance (EMD) as loss functions and evaluation metrics. We train and evaluate our models using the newly released nuScenes dataset, and characterize their performance and complexity with several baselines. Compared to directly using FlowNet3D, our proposed architectures achieve CD and EMD nearly an order of magnitude lower. In addition, we show that our predictions generate reasonable scene flow approximations without using any labelled supervision.