# TPCN: Temporal Point Cloud Networks for Motion Forecasting

arXiv: https://arxiv.org/abs/2103.03067 · HTML: https://mlsanity.com/paper/2103.03067/

## Key facts

- Cited by 94 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 38 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://mlsanity.com/paper/2007.16100/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [Acquisition of Localization Confidence for Accurate Object Detection](https://mlsanity.com/paper/1807.11590/) (2018)
- [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021)
- [Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting](https://mlsanity.com/paper/1910.03650/) (2019)
- [HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection](https://mlsanity.com/paper/2003.00186/) (2020)
- [TPNet: Trajectory Proposal Network for Motion Prediction](https://mlsanity.com/paper/2004.12255/) (2020)

## Built on by (most cited first, 30 of 94)

- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [HOME: Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2105.10968/) (2021)
- [HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding](https://mlsanity.com/paper/2205.09753/) (2022)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://mlsanity.com/paper/2110.06607/) (2021)
- [Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders](https://mlsanity.com/paper/2308.09882/) (2023)
- [Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving](https://mlsanity.com/paper/2210.08061/) (2022)
- [LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints](https://mlsanity.com/paper/2302.13933/) (2023)
- [GANet: Goal Area Network for Motion Forecasting](https://mlsanity.com/paper/2209.09723/) (2022)
- [ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals](https://mlsanity.com/paper/2303.12071/) (2023)
- [Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2305.14715/) (2023)
- [Traj-MAE: Masked Autoencoders for Trajectory Prediction](https://mlsanity.com/paper/2303.06697/) (2023)
- [FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs](https://mlsanity.com/paper/2211.16197/) (2022)
- [ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation](https://mlsanity.com/paper/2307.14187/) (2023)
- [HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention](https://mlsanity.com/paper/2404.06351/) (2024)
- [PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving](https://mlsanity.com/paper/2311.08100/) (2023)
- [SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving](https://mlsanity.com/paper/2206.14116/) (2022)
- [MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction](https://mlsanity.com/paper/2308.10280/) (2023)
- [Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/2303.04364/) (2023)
- [BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2312.06371/) (2023)
- [R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement](https://mlsanity.com/paper/2211.08609/) (2022)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [DRINet++: Efficient Voxel-as-point Point Cloud Segmentation](https://mlsanity.com/paper/2111.08318/) (2021)
- [Path-Aware Graph Attention for HD Maps in Motion Prediction](https://mlsanity.com/paper/2202.13772/) (2022)
- [Bootstrap Motion Forecasting With Self-Consistent Constraints](https://mlsanity.com/paper/2204.05859/) (2022)

## Abstract (arXiv)

We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches that rasterize agents and map information as 2D images or operate in a graph representation, our approach extends ideas from point cloud learning with dynamic temporal learning to capture both spatial and temporal information by splitting trajectory prediction into both spatial and temporal dimensions. In the spatial dimension, agents can be viewed as an unordered point set, and thus it is straightforward to apply point cloud learning techniques to model agents' locations. While the spatial dimension does not take kinematic and motion information into account, we further propose dynamic temporal learning to model agents' motion over time. Experiments on the Argoverse motion forecasting benchmark show that our approach achieves the state-of-the-art results.