3D Object Tracking
33 papers with code • 2 benchmarks • 11 datasets
3D Object Tracking is a computer vision task dedicated to monitoring and precisely locating objects as they navigate within a three-dimensional environment. It frequently utilizes 3D object detection techniques to pinpoint the objects and establish unique identifications that persist across multiple frames.
Benchmarks
These leaderboards are used to track progress in 3D Object Tracking
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Libraries
Use these libraries to find 3D Object Tracking models and implementationsDatasets
KITTI
Argoverse
K-Radar
MOTFront
TrackML challenge Throughput phase dataset
IBISCape
DTTD
3D-POP
RTB
Most implemented papers
Leveraging Shape Completion for 3D Siamese Tracking
SilvioGiancola/ShapeCompletion3DTracking
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CVPR 2019
We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation.
RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom.
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
google-research-datasets/Objectron
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CVPR 2021
3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval.
Monocular Quasi-Dense 3D Object Tracking
SysCV/qd-3dt
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Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios.
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching.
3D Object Tracking with Transformer
3bobo/lttr
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By using cross-attention, the transformer decoder fuses features and includes more target cues into the current point cloud feature to compute the region attentions, which makes the similarity computing more efficient.
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified.
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects
Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision.
