Papers 3D Multi-Object Tracking
“3D Multi-Object Tracking” 태그가 달린 논문 113편 · 필터 해제
CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of ca…
3D Multi-Object Tracking3D Object DetectionAutonomous DrivingMulti-Object Tracking+3Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking
3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception. However, current 3D trackers face issues with accuracy and latency consistency. I…
3D Multi-Object TrackingCPUMulti-Object TrackingObject TrackingUnleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception
Low-cost, vision-centric 3D perception systems for autonomous driving have made significant progress in recent years, narrowing the gap to expensive LiDAR-based methods. The primary challenge in becoming a fully reliable…
3D Multi-Object Tracking3D Object Detection3D Object Detection (RoI)3D Semantic Occupancy Prediction+3ShaSTA-Fuse: Camera-LiDAR Sensor Fusion to Model Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking
3D multi-object tracking (MOT) is essential for an autonomous mobile agent to safely navigate a scene. In order to maximize the perception capabilities of the autonomous agent, we aim to develop a 3D MOT framework that f…
3D Multi-Object TrackingMulti-Object TrackingNavigateObject Tracking+1Offline Tracking with Object Permanence
To reduce the expensive labor cost for manual labeling autonomous driving datasets, an alternative is to automatically label the datasets using an offline perception system. However, objects might be temporally occluded.…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject+13D Multiple Object Tracking on Autonomous Driving: A Literature Review
3D multi-object tracking (3D MOT) stands as a pivotal domain within autonomous driving, experiencing a surge in scholarly interest and commercial promise over recent years. Despite its paramount significance, 3D MOT conf…
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingMultiple Object Tracking+1Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter
Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework's reliability could be limited by occlusion or sensor failure. To address th…
3D Multi-Object TrackingAutonomous DrivingAutonomous VehiclesWhich Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?
Online 3D multi-object tracking (MOT) has recently received significant research interests due to the expanding demand of 3D perception in advanced driver assistance systems (ADAS) and autonomous driving (AD). Among the …
3D Multi-Object TrackingAutonomous DrivingMulti-Object TrackingObject TrackingDelving into Motion-Aware Matching for Monocular 3D Object Tracking
Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of objects along different time frames is critica…
3D Multi-Object Tracking3D Object Detection3D Object TrackingMonocular 3D Object Detection+53DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning. Based on the substantial progress in object d…
3D Multi-Object TrackingAutonomous VehiclesEdge ClassificationMotion Planning+6FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels
LiDAR-based fully sparse architecture has garnered increasing attention. FSDv1 stands out as a representative work, achieving impressive efficacy and efficiency, albeit with intricate structures and handcrafted designs. …
3D Multi-Object Tracking3D Object DetectionClusteringInductive Bias+4DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to …
3D Multi-Object Tracking3D Object DetectionObjectobject-detection+1TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses
3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent …
3D Multi-Object Tracking3D Object TrackingAutonomous DrivingMulti-Object Tracking+2Object Re-Identification from Point Clouds
Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autonomous driving, robotics, etc.). However, …
3D Multi-Object TrackingAutonomous DrivingImage RetrievalMulti-Object Tracking+3You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve satisfactory tracking performance. In th…
3D Multi-Object TrackingMulti-Object TrackingObjectobject-detection+3CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation. Although recent low-cost camera-based approaches have shown promising results, they are…
3D Multi-Object Tracking3D Object Detection3D Object TrackingAutonomous Driving+2ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitable changing of detection scores leads to …
3D Multi-Object Trackingmotion predictionMulti-Object TrackingObject+1Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection
In this paper, we propose a long-sequence modeling framework, named StreamPETR, for multi-view 3D object detection. Built upon the sparse query design in the PETR series, we systematically develop an object-centric tempo…
3D Multi-Object Tracking3D Object DetectionObjectobject-detection+1Virtual Sparse Convolution for Multimodal 3D Object Detection
Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, int…
3D Multi-Object Tracking3D Object DetectionDepth CompletionMultiple Object Tracking+43D Multi-Object Tracking Based on Uncertainty-Guided Data Association
In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage. Nam…
3D Multi-Object TrackingMulti-Object TrackingObject Tracking