Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking

• URL: http://arxiv.org/abs/2606.07233v1
• Date: Fri, 05 Jun 2026 13:00:04 GMT
• Title: Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking
• Abstract summary: This work presents a systematic study of image-based ReID in online 3D MOT, utilizing a lightweight projection-based framework.<n>Experiments on the Pedestrian class of the KITTI dataset reveal that naive linear fusion, of appearance and motion costs, degrades performance due to visual noise.<n>We show that lightweight architectures can offer an optimal trade-off between the low latency required for safe navigation and the discriminative power needed for social awareness.
• Score: 0.6117371161379209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments. While integrating RGB-based Re-Identification (ReID) offers a theoretical solution for preserving identity context, existing approaches often rely on computationally expensive parallel detectors that hinder real-time robot responsiveness. This work presents a systematic study of image-based ReID in online 3D MOT, utilizing a lightweight projection-based framework to decouple geometric and appearance modeling for mobile robots. A comprehensive analysis of feature extraction architectures is conducted, employing lightweight CNNs and Vision Transformers, and evaluating various multi-modal data association strategies to balance computational latency with robust tracking. Experiments on the Pedestrian class of the KITTI dataset reveal that naive linear fusion, of appearance and motion costs, degrades performance due to visual noise. Conversely, a cascaded matching strategy successfully recovers occluded tracks without compromising overall precision, effectively preventing identity switches to maintain human-robot interaction continuity. We show that lightweight architectures can offer an optimal trade-off between the low latency required for safe navigation and the discriminative power needed for social awareness.

Related papers

• View-Adaptive Renderer for View-Consistent 2D-to-3D Generation [2.8951915538170288]
  Reconstructing 3D shapes from a single image remains a fundamental yet challenging problem in computer vision.<n>Traditional monocular 3D generation pipelines typically synthesize multiple views from a single input image before applying Neural Radiance Field (NeRF)-based reconstruction.<n>We propose a novel viewpoint-adaptive rendering neural framework that enables robust 3D reconstruction even when given partially inconsistent multi-view inputs.
  arXiv  Detail & Related papers  (2026-08-10T04:28:08Z)
• Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception [0.0]
  In this paper, we propose a method for scaling variability in 3D pedestrian assets for synthetic scene generation.<n>We generate multiple distinct pedestrian instances by synthesizing diverse facial textures and identity-level appearance variations.<n>Using the assets, we construct synthetic datasets and study the impact of mixing real and synthetic data for RGB-based object detection.
  arXiv  Detail & Related papers  (2026-05-13T16:35:50Z)
• Real-Time Structural Detection for Indoor Navigation from 3D LiDAR Using Bird's-Eye-View Images [0.0]
  Existing 3D methods are computationally prohibitive, while traditional 2D geometric approaches lack robustness.<n>This paper presents a real-time framework that projects 3D LiDAR data into 2D Bird's-Eye-View images to enable efficient detection of structural elements relevant to mapping and navigation.
  arXiv  Detail & Related papers  (2026-03-20T10:15:49Z)
• Online Segment Any 3D Thing as Instance Tracking [60.20416622842975]
  We reconceptualize online 3D segmentation as an instance tracking problem (AutoSeg3D)<n>We introduce spatial consistency learning to mitigate the fragmentation problem inherent in Vision Foundation Models.<n>Our method establishes a new state-of-the-art, surpassing ESAM by 2.8 AP on ScanNet200.
  arXiv  Detail & Related papers  (2025-12-08T14:48:51Z)
• DVLO4D: Deep Visual-Lidar Odometry with Sparse Spatial-temporal Fusion [28.146811420532455]
  We introduce DVLO4D, a novel visual-LiDAR odometry framework that leverages sparse spatial-temporal fusion to enhance accuracy and robustness.<n>Our method has high efficiency, with an inference time of 82 ms, possessing the potential for the real-time deployment.
  arXiv  Detail & Related papers  (2025-09-07T11:43:11Z)
• Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking [43.11267507022928]
  This paper proposes a novel mitigation framework on 3D LiDAR scene against adversarial noise.<n>We employ the least-squares graph tool to reduce the induced positional error of each detection's centroid.<n>An extensive evaluation study on the real-world V2V4Real dataset demonstrates that the proposed method significantly outperforms both single and multi-agent tracking frameworks.
  arXiv  Detail & Related papers  (2025-07-07T08:41:08Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation [113.35352122662752]
  We present an efficient multi-modal backbone for outdoor 3D perception named UniTR. UniTR processes a variety of modalities with unified modeling and shared parameters. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks.
  arXiv  Detail & Related papers  (2023-08-15T12:13:44Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.