# LidarGait: Benchmarking 3D Gait Recognition with Point Clouds

arXiv: https://arxiv.org/abs/2211.10598 · HTML: https://mlsanity.com/paper/2211.10598/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 19 Nov 2022.

## Builds on (most cited first, 29 of 29)

- [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)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline](https://mlsanity.com/paper/2106.05304/) (2021)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://mlsanity.com/paper/1608.07916/) (2016)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [Waymo Open Dataset: Panoramic Video Panoptic Segmentation](https://mlsanity.com/paper/2206.07704/) (2022)
- [GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality](https://mlsanity.com/paper/2203.03972/) (2022)
- [Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloud](https://mlsanity.com/paper/2207.11919/) (2022)
- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection](https://mlsanity.com/paper/2106.08827/) (2021)
- [PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation](https://mlsanity.com/paper/2101.06175/) (2021)
- [STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes](https://mlsanity.com/paper/2204.01026/) (2022)
- [Parameter-Efficient Person Re-identification in the 3D Space](https://mlsanity.com/paper/2006.04569/) (2020)
- [Gait Recognition in the Wild with Multi-hop Temporal Switch](https://mlsanity.com/paper/2209.00355/) (2022)
- [Gait Recognition with Mask-based Regularization](https://mlsanity.com/paper/2203.04038/) (2022)
- [VersatileGait: A Large-Scale Synthetic Gait Dataset with Fine-GrainedAttributes and Complicated Scenarios](https://mlsanity.com/paper/2101.01394/) (2021)
- [Multi-scale Context-aware Network with Transformer for Gait Recognition](https://mlsanity.com/paper/2204.03270/) (2022)
- [Evidence-based Match-status-Aware Gait Recognition for Out-of-Gallery Gait Identification](https://mlsanity.com/paper/2211.08007/) (2022)

## Built on by (most cited first, 30 of 55)

- [Exploring Deep Models for Practical Gait Recognition](https://mlsanity.com/paper/2303.03301/) (2023)
- [BigGait: Learning Gait Representation You Want by Large Vision Models](https://mlsanity.com/paper/2402.19122/) (2024)
- [SkeletonGait: Gait Recognition Using Skeleton Maps](https://mlsanity.com/paper/2311.13444/) (2023)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Cross-Covariate Gait Recognition: A Benchmark](https://mlsanity.com/paper/2312.14404/) (2023)
- [BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models](https://mlsanity.com/paper/2505.18132/) (2025)
- [Gait Recognition in Large-scale Free Environment via Single LiDAR](https://mlsanity.com/paper/2211.12371/) (2022)
- [QAGait: Revisit Gait Recognition from a Quality Perspective](https://mlsanity.com/paper/2401.13531/) (2024)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [On Denoising Walking Videos for Gait Recognition](https://mlsanity.com/paper/2505.18582/) (2025)
- [ShARc: Shape and Appearance Recognition for Person Identification In-the-wild](https://mlsanity.com/paper/2310.15946/) (2023)
- [Open-Set Biometrics: Beyond Good Closed-Set Models](https://mlsanity.com/paper/2407.16133/) (2024)
- [Exploring More from Multiple Gait Modalities for Human Identification](https://mlsanity.com/paper/2412.11495/) (2024)
- [Camera-LiDAR Cross-modality Gait Recognition](https://mlsanity.com/paper/2407.02038/) (2024)
- [Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification](https://mlsanity.com/paper/2404.04120/) (2024)
- [HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID](https://mlsanity.com/paper/2508.05038/) (2025)
- [GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation](https://mlsanity.com/paper/2311.16497/) (2023)
- [DAGait: Generalized Skeleton-Guided Data Alignment for Gait Recognition](https://mlsanity.com/paper/2503.18830/) (2025)
- [Language-Guided and Motion-Aware Gait Representation for Generalizable Recognition](https://mlsanity.com/paper/2601.11931/) (2026)
- [Beyond Role-Based Surgical Domain Modeling: Generalizable Re-Identification in the Operating Room](https://mlsanity.com/paper/2503.13028/) (2025)
- [GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences](https://mlsanity.com/paper/2508.07782/) (2025)
- [TrackletGait: A Robust Framework for Gait Recognition in the Wild](https://mlsanity.com/paper/2508.02143/) (2025)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [EventGait: Towards Robust Gait Recognition with Event Streams](https://mlsanity.com/paper/2605.22139/) (2026)
- [Accurate Quantization for Gait Representation Learning](https://mlsanity.com/paper/2405.13859/) (2024)
- [LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition](https://mlsanity.com/paper/2406.12355/) (2024)
- [SpheriGait: Enriching Spatial Representation via Spherical Projection for LiDAR-based Gait Recognition](https://mlsanity.com/paper/2409.11869/) (2024)
- [GaitAdapt: Continual Learning for Evolving Gait Recognition](https://mlsanity.com/paper/2508.03375/) (2025)
- [Mesh-Gait: A Unified Framework for Gait Recognition Through Multi-Modal Representation Learning from 2D Silhouettes](https://mlsanity.com/paper/2510.10406/) (2025)
- [Combo-Gait: Unified Transformer Framework for Multi-Modal Gait Recognition and Attribute Analysis](https://mlsanity.com/paper/2510.10417/) (2025)

## Abstract (arXiv)

Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we built the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://lidargait.github.io.