# SSFlowNet: Semi-supervised Scene Flow Estimation On Point Clouds With Pseudo Label

arXiv: https://arxiv.org/abs/2312.15271 · HTML: https://mlsanity.com/paper/2312.15271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 23 of 23)

- [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)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://mlsanity.com/paper/1806.01411/) (2018)
- [FLOT: Scene Flow on Point Clouds Guided by Optimal Transport](https://mlsanity.com/paper/2007.11142/) (2020)
- [HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds](https://mlsanity.com/paper/1906.05332/) (2019)
- [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://mlsanity.com/paper/1901.01060/) (2019)
- [Semantic Video Segmentation by Gated Recurrent Flow Propagation](https://mlsanity.com/paper/1612.08871/) (2016)
- [FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation](https://mlsanity.com/paper/2011.10147/) (2020)
- [Learning Video Object Segmentation with Visual Memory](https://mlsanity.com/paper/1704.05737/) (2017)
- [Differentiable Manifold Reconstruction for Point Cloud Denoising](https://mlsanity.com/paper/2007.13551/) (2020)
- [Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk](https://mlsanity.com/paper/2105.08248/) (2021)
- [PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds](https://mlsanity.com/paper/1911.12408/) (2019)
- [What Matters for 3D Scene Flow Network](https://mlsanity.com/paper/2207.09143/) (2022)
- [Local Memory Attention for Fast Video Semantic Segmentation](https://mlsanity.com/paper/2101.01715/) (2021)
- [Self-Supervised 3D Scene Flow Estimation Guided by Superpoints](https://mlsanity.com/paper/2305.02528/) (2023)
- [PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation](https://mlsanity.com/paper/1904.06116/) (2019)
- [MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory](https://mlsanity.com/paper/2311.01556/) (2023)
- [Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds](https://mlsanity.com/paper/2104.04724/) (2021)
- [Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels](https://mlsanity.com/paper/2203.12655/) (2022)
- [Edge Aware Learning for 3D Point Cloud](https://mlsanity.com/paper/2309.13472/) (2023)

## Built on by (most cited first, 1 of 1)

- [Open 3D World in Autonomous Driving](https://mlsanity.com/paper/2408.10880/) (2024)

## Abstract (arXiv)

In the domain of supervised scene flow estimation, the process of manual labeling is both time-intensive and financially demanding. This paper introduces SSFlowNet, a semi-supervised approach for scene flow estimation, that utilizes a blend of labeled and unlabeled data, optimizing the balance between the cost of labeling and the precision of model training. SSFlowNet stands out through its innovative use of pseudo-labels, mainly reducing the dependency on extensively labeled datasets while maintaining high model accuracy. The core of our model is its emphasis on the intricate geometric structures of point clouds, both locally and globally, coupled with a novel spatial memory feature. This feature is adept at learning the geometric relationships between points over sequential time frames. By identifying similarities between labeled and unlabeled points, SSFlowNet dynamically constructs a correlation matrix to evaluate scene flow dependencies at individual point level. Furthermore, the integration of a flow consistency module within SSFlowNet enhances its capability to consistently estimate flow, an essential aspect for analyzing dynamic scenes. Empirical results demonstrate that SSFlowNet surpasses existing methods in pseudo-label generation and shows adaptability across varying data volumes. Moreover, our semi-supervised training technique yields promising outcomes even with different smaller ratio labeled data, marking a substantial advancement in the field of scene flow estimation.