# SpheriGait: Enriching Spatial Representation via Spherical Projection for LiDAR-based Gait Recognition

arXiv: https://arxiv.org/abs/2409.11869 · HTML: https://mlsanity.com/paper/2409.11869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 21 of 21)

- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [LidarGait: Benchmarking 3D Gait Recognition with Point Clouds](https://mlsanity.com/paper/2211.10598/) (2022)
- [Exploring Deep Models for Practical Gait Recognition](https://mlsanity.com/paper/2303.03301/) (2023)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [SkeletonGait: Gait Recognition Using Skeleton Maps](https://mlsanity.com/paper/2311.13444/) (2023)
- [GPGait: Generalized Pose-based Gait Recognition](https://mlsanity.com/paper/2303.05234/) (2023)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark](https://mlsanity.com/paper/2206.13964/) (2022)
- [Gait Recognition in Large-scale Free Environment via Single LiDAR](https://mlsanity.com/paper/2211.12371/) (2022)
- [ShARc: Shape and Appearance Recognition for Person Identification In-the-wild](https://mlsanity.com/paper/2310.15946/) (2023)
- [LiDAR-based Person Re-identification](https://mlsanity.com/paper/2312.03033/) (2023)
- [A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition](https://mlsanity.com/paper/2312.14410/) (2023)
- [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)
- [GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation](https://mlsanity.com/paper/2311.16497/) (2023)
- [HybridGait: A Benchmark for Spatial-Temporal Cloth-Changing Gait Recognition with Hybrid Explorations](https://mlsanity.com/paper/2401.00271/) (2023)
- [Weighted Bilinear Coding over Salient Body Parts for Person Re-identification](https://mlsanity.com/paper/1803.08580/) (2018)
- [Distribution Context Aware Loss for Person Re-identification](https://mlsanity.com/paper/1911.07273/) (2019)
- [Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification](https://mlsanity.com/paper/2112.05626/) (2021)

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

- [HorGait: A Hybrid Model for Accurate Gait Recognition in LiDAR Point Cloud Planar Projections](https://mlsanity.com/paper/2410.08454/) (2024)

## Abstract (arXiv)

Gait recognition is a rapidly progressing technique for the remote identification of individuals. Prior research predominantly employing 2D sensors to gather gait data has achieved notable advancements; nonetheless, they have unavoidably neglected the influence of 3D dynamic characteristics on recognition. Gait recognition utilizing LiDAR 3D point clouds not only directly captures 3D spatial features but also diminishes the impact of lighting conditions while ensuring privacy protection.The essence of the problem lies in how to effectively extract discriminative 3D dynamic representation from point clouds.In this paper, we proposes a method named SpheriGait for extracting and enhancing dynamic features from point clouds for Lidar-based gait recognition. Specifically, it substitutes the conventional point cloud plane projection method with spherical projection to augment the perception of dynamic feature.Additionally, a network block named DAM-L is proposed to extract gait cues from the projected point cloud data. We conducted extensive experiments and the results demonstrated the SpheriGait achieved state-of-the-art performance on the SUSTech1K dataset, and verified that the spherical projection method can serve as a universal data preprocessing technique to enhance the performance of other LiDAR-based gait recognition methods, exhibiting exceptional flexibility and practicality.