# Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice

arXiv: https://arxiv.org/abs/2203.15469 · HTML: https://mlsanity.com/paper/2203.15469/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [Tangent Convolutions for Dense Prediction in 3D](https://mlsanity.com/paper/1807.02443/) (2018)
- [Pixel-Adaptive Convolutional Neural Networks](https://mlsanity.com/paper/1904.05373/) (2019)
- [Fully-Convolutional Point Networks for Large-Scale Point Clouds](https://mlsanity.com/paper/1808.06840/) (2018)
- [LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices](https://mlsanity.com/paper/1912.05905/) (2019)
- [LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment](https://mlsanity.com/paper/2103.02263/) (2021)
- [LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices](https://mlsanity.com/paper/2108.03917/) (2021)

## Built on by (most cited first, 9 of 9)

- [Mask4Former: Mask Transformer for 4D Panoptic Segmentation](https://mlsanity.com/paper/2309.16133/) (2023)
- [MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds](https://mlsanity.com/paper/2307.09316/) (2023)
- [SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation](https://mlsanity.com/paper/2308.13323/) (2023)
- [SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net](https://mlsanity.com/paper/2403.08885/) (2024)
- [SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud](https://mlsanity.com/paper/2406.16279/) (2024)
- [Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2410.06893/) (2024)
- [LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention](https://mlsanity.com/paper/2301.04275/) (2023)
- [A Preprocessing and Postprocessing Voxel-based Method for LiDAR Semantic Segmentation Improvement in Long Distance](https://mlsanity.com/paper/2405.10046/) (2024)
- [MFSeg: Efficient Multi-frame 3D Semantic Segmentation](https://mlsanity.com/paper/2505.04408/) (2025)

## Abstract (arXiv)

Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation and interaction with the environment. Approaches which use only one time-step of data cannot distinguish between moving objects nor can they benefit from temporal integration. In this work, we extend a backbone LatticeNet to process temporal point cloud data. Additionally, we take inspiration from optical flow methods and propose a new module called Abstract Flow which allows the network to match parts of the scene with similar abstract features and gather the information temporally. We obtain state-of-the-art results on the SemanticKITTI dataset that contains LiDAR scans from real urban environments. We share the PyTorch implementation of TemporalLatticeNet at https://github.com/AIS-Bonn/temporal_latticenet .