# Parameter-Efficient Person Re-identification in the 3D Space

arXiv: https://arxiv.org/abs/2006.04569 · HTML: https://mlsanity.com/paper/2006.04569/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 30 of 33)

- [LidarGait: Benchmarking 3D Gait Recognition with Point Clouds](https://mlsanity.com/paper/2211.10598/) (2022)
- [Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification](https://mlsanity.com/paper/2308.10658/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments](https://mlsanity.com/paper/2407.13640/) (2024)
- [Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking](https://mlsanity.com/paper/2407.13646/) (2024)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)
- [AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification](https://mlsanity.com/paper/2503.08121/) (2025)
- [Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification](https://mlsanity.com/paper/2305.13659/) (2023)
- [HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID](https://mlsanity.com/paper/2508.05038/) (2025)
- [StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition](https://mlsanity.com/paper/2212.12857/) (2022)
- [Progressive Text-to-3D Generation for Automatic 3D Prototyping](https://mlsanity.com/paper/2309.14600/) (2023)
- [Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes](https://mlsanity.com/paper/2109.00452/) (2021)
- [Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras](https://mlsanity.com/paper/2401.15616/) (2024)
- [LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2410.13618/) (2024)
- [3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective](https://mlsanity.com/paper/2204.13096/) (2022)
- [Scale-adaptive UAV Geo-localization via Height-aware Partition Learning](https://mlsanity.com/paper/2412.11535/) (2024)
- [A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification](https://mlsanity.com/paper/2207.08387/) (2022)
- [Object Re-Identification from Point Clouds](https://mlsanity.com/paper/2305.10210/) (2023)
- [Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2402.03716/) (2024)
- [Minimizing the Pretraining Gap: Domain-aligned Text-Based Person Retrieval](https://mlsanity.com/paper/2507.10195/) (2025)
- [3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground Re-Identification](https://mlsanity.com/paper/2604.26520/) (2026)
- [Robust Person Identification: A WiFi Vision-based Approach](https://mlsanity.com/paper/2210.00127/) (2022)
- [CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras](https://mlsanity.com/paper/2602.18047/) (2026)
- [Revisiting the Role of Texture in 3D Person Re-identification](https://mlsanity.com/paper/2410.00348/) (2024)
- [See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2412.01345/) (2024)
- [Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data](https://mlsanity.com/paper/2504.13077/) (2025)
- [TIGeR: Text-Instructed Generation and Refinement for Template-Free Hand-Object Interaction](https://mlsanity.com/paper/2506.00953/) (2025)

## Abstract (arXiv)

People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting the understanding of people. In this work, we address this limitation by exploring the prior knowledge of the 3D body structure. Specifically, we project 2D images to a 3D space and introduce a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the pedestrian representation directly from 3D point clouds. OG-Net effectively exploits the local information provided by sparse 3D points and takes advantage of the structure and appearance information in a coherent manner. With the help of 3D geometry information, we can learn a new type of deep re-id feature free from noisy variants, such as scale and viewpoint. To our knowledge, we are among the first attempts to conduct person re-identification in the 3D space. We demonstrate through extensive experiments that the proposed method (1) eases the matching difficulty in the traditional 2D space, (2) exploits the complementary information of 2D appearance and 3D structure, (3) achieves competitive results with limited parameters on four large-scale person re-id datasets, and (4) has good scalability to unseen datasets. Our code, models and generated 3D human data are publicly available at https://github.com/layumi/person-reid-3d .