# PointINet: Point Cloud Frame Interpolation Network

arXiv: https://arxiv.org/abs/2012.10066 · HTML: https://mlsanity.com/paper/2012.10066/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2110.04076/) (2021)
- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation](https://mlsanity.com/paper/2303.15126/) (2023)
- [IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment](https://mlsanity.com/paper/2203.11590/) (2022)
- [Be Careful with Rotation: A Uniform Backdoor Pattern for 3D Shape](https://mlsanity.com/paper/2211.16192/) (2022)
- [FastPCI: Motion-Structure Guided Fast Point Cloud Frame Interpolation](https://mlsanity.com/paper/2410.19573/) (2024)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation](https://mlsanity.com/paper/2405.14241/) (2024)
- [RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network](https://mlsanity.com/paper/2106.00496/) (2021)
- [Virtually increasing the measurement frequency of LIDAR sensor utilizing a single RGB camera](https://mlsanity.com/paper/2302.05192/) (2023)
- [FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving](https://mlsanity.com/paper/2112.04401/) (2021)
- [UniRED: Unified RGB-D Video Frame Interpolation with Event Guidance](https://mlsanity.com/paper/2606.24282/) (2026)

## Abstract (arXiv)

LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named Point Cloud Frame Interpolation is studied in this paper. Given two consecutive point cloud frames, Point Cloud Frame Interpolation aims to generate intermediate frame(s) between them. To achieve that, we propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates. We start by estimating bi-directional 3D scene flow between the two point clouds and then warp them to the given time step based on the 3D scene flow. To fuse the two warped frames and generate intermediate point cloud(s), we propose a novel learning-based points fusion module, which simultaneously takes two warped point clouds into consideration. We design both quantitative and qualitative experiments to evaluate the performance of the point cloud frame interpolation method and extensive experiments on two large scale outdoor LiDAR datasets demonstrate the effectiveness of the proposed PointINet. Our code is available at https://github.com/ispc-lab/PointINet.git.