Papers 3D Multi-Object Tracking
“3D Multi-Object Tracking” 태그가 달린 논문 112편 · 필터 해제
Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking
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 environmen…
3D Multi-Object TrackingWeakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation
Due to the difficulty of obtaining ground-truth data for 4D radar scene flow estimation, previous methods typically rely on either self-supervised losses or cross-modal supervision using 3D LiDAR data, 2D images, and odo…
3D Multi-Object TrackingScene Flow EstimationPoint CloudsEfficient Online 3D Multi-Camera Multi-Object Tracking and Pose Estimation
This paper proposes a fast and online method for jointly performing 3D multi-object tracking and pose estimation using multiple monocular cameras. Our algorithm requires only 2D bounding box and pose detections, eliminat…
3D Multi-Object TrackingComputational EfficiencyPose EstimationRadar-Informed 3D Multi-Object Tracking under Adverse Conditions
The challenge of 3D multi-object tracking is achieving robustness in real-world applications, for example under adverse conditions and maintaining consistency as distance increases. To overcome these challenges, sensor f…
3D Multi-Object TrackingPoint CloudsS3KF: Spherical State-Space Kalman Filtering for Panoramic 3D Multi-Object Tracking
Panoramic multi-object tracking is important for industrial safety monitoring, wide-area robotic perception, and infrastructure-light deployment in large workspaces. In these settings, the sensing system must provide ful…
3D Multi-Object TrackingDepth EstimationFusion-Poly: A Polyhedral Framework Based on Spatial-Temporal Fusion for 3D Multi-Object Tracking
LiDAR-camera 3D multi-object tracking (MOT) combines rich visual semantics with accurate depth cues to improve trajectory consistency and tracking reliability. In practice, however, LiDAR and cameras operate at different…
3D Multi-Object TrackingNOVA: Next-step Open-Vocabulary Autoregression for 3D Multi-Object Tracking in Autonomous Driving
Generalizing across unknown targets is critical for open-world perception, yet existing 3D Multi-Object Tracking (3D MOT) pipelines remain limited by closed-set assumptions and ``semantic-blind'' heuristics. To address t…
3D Multi-Object TrackingAutonomous DrivingOffline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking
Offline 3D multi-object tracking (MOT) is a critical component of the 4D auto-labeling (4DAL) process. It enhances pseudo-labels generated by high-performance detectors through the incorporation of temporal context. Howe…
3D Multi-Object TrackingLAA3D: A Benchmark of Detecting and Tracking Low-Altitude Aircraft in 3D Space
Perception of Low-Altitude Aircraft (LAA) in 3D space enables precise 3D object localization and behavior understanding. However, datasets tailored for 3D LAA perception remain scarce. To address this gap, we present LAA…
3D Multi-Object TrackingObject Localization3D Object DetectionPose EstimationDelving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking
3D multi-object tracking is a critical and challenging task in the field of autonomous driving. A common paradigm relies on modeling individual object motion, e.g., Kalman filters, to predict trajectories. While effectiv…
3D Multi-Object TrackingAutonomous DrivingCoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception
Cooperative perception aims to address the inherent limitations of single-vehicle autonomous driving systems through information exchange among multiple agents. Previous research has primarily focused on single-frame per…
3D Multi-Object TrackingAutonomous DrivingTowards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking
This work addresses the critical lack of precision in state estimation in the Kalman filter for 3D multi-object tracking (MOT) and the ongoing challenge of selecting the appropriate motion model. Existing literature comm…
3D Multi-Object TrackingMulti-Object TrackingMultiple Object TrackingNavigate+5Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse
As the agricultural workforce declines and labor costs rise, robotic yield estimation has become increasingly important. While unmanned ground vehicles (UGVs) are commonly used for indoor farm monitoring, their deploymen…
3D Multi-Object TrackingMulti-Object TrackingObject TrackingTQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking
Query denoising has become a standard training strategy for DETR-based detectors by addressing the slow convergence issue. Besides that, query denoising can be used to increase the diversity of training samples for model…
3D Multi-Object TrackingDenoisingMulti-Object TrackingObject TrackingOptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering
Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressi…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject TrackingEasy-Poly: A Easy Polyhedral Framework For 3D Multi-Object Tracking
Recent advancements in 3D multi-object tracking (3D MOT) have predominantly relied on tracking-by-detection pipelines. However, these approaches often neglect potential enhancements in 3D detection processes, leading to …
3D Multi-Object TrackingAutonomous DrivingData AugmentationManagement+2IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter
3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects, assisting robots or vehicles in smarter path planning and obstacle avoidance. Existing 3D MOT methods based on the Tracking-by-Detection fr…
3D Multi-Object TrackingMulti-Object TrackingObject TrackingHybridTrack: A Hybrid Approach for Robust Multi-Object Tracking
The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined…
3D Multi-Object TrackingMulti-Object TrackingObject TrackingGRAE-3DMOT: Geometry Relation-Aware Encoder for Online 3D Multi-Object Tracking
Recently, 3D multi-object tracking (MOT) has widely adopted the standard tracking-by-detection paradigm, which solves the association problem between detections and tracks. Many tracking-by-detection approaches estab…
3D Multi-Object TrackingMulti-Object TrackingObject TrackingRelationSpaRC: Sparse Radar-Camera Fusion for 3D Object Detection
In this work, we present SpaRC, a novel Sparse fusion transformer for 3D perception that integrates multi-view image semantics with Radar and Camera point features. The fusion of radar and camera modalities has emerged a…
3D Multi-Object Tracking3D Object DetectionAutonomous DrivingDepth Estimation+3