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computer-vision-1

3D Object Tracking

3260 papers • 126 benchmarks • 313 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.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in 3d-object-tracking-1

Trend

Dataset

Best Model

Actions

RTB

RTB

Argoverse CVPR 2020

Argoverse CVPR 2020

Libraries

i

Use these libraries to find 3d-object-tracking-1 models and implementations

dlr-rm/3dobjecttracking

3 papers 591

Datasets

KITTI

Argoverse

K-Radar

MOTFront

TrackML challenge Throughput phase dataset

TrackML challenge Throughput phase dataset

IBISCape

Subtasks

3D Single Object Tracking

Most implemented papers

Center-based 3D Object Detection and Tracking

Philipp Krähenbühl, Xingyi Zhou, Tianwei Yin•Thu Jun 18 2020

The framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity, and refines these estimates using additional point features on the object.

2043

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

wenbowen123/BundleTrack

2 papers 569

DTTD

DTTD

3D-POP

RTB

The RBO Dataset of Articulated Objects and Interactions

The RBO Dataset of Articulated Objects and Interactions

0

Argoverse: 3D Tracking and Forecasting With Rich Maps

S. Lucey, Peter Carr, James Hays, Deva Ramanan, Jagjeet Singh, Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Sławomir Bąk, Andrew Hartnett, De Wang•Fri May 31 2019

Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps, which contain rich geometric and semantic metadata which are not currently available in any public dataset.

1509 0

Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications

Weiyu Feng, Seth Z. Zhao, Chuanyu Pan, Adam Chang, Yichen Chen, Zekun Wang, A. Yang•Sat Feb 11 2023

This work creates a novel RGB-D dataset, called Digital Twin Tracking Dataset (DTTD), to enable further research of the problem and extend potential solutions towards longer ranges and mm localization accuracy, and demonstrates that DTTD can help researchers develop future object tracking methods and analyze new challenges.

11 0

P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

ZHIGUO CAO, Yang Xiao, Haozhe Qi, Chen Feng, Feng Zhao•Wed May 27 2020

This work first localize potential target centers in 3D search area embedded with target information, then point-driven 3D target proposal and verification are executed jointly, so the time-consuming 3D exhaustive search can be avoided.

169 0

SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World

Rudolph Triebel, Manuel Stoiber, Martin Pfanne, Klaus H. Strobl, A. Albu-Schaffer•Sun Oct 24 2021

SRT3D is developed, a sparse region-based approach to 3D object tracking that improves on the current state of the art both in terms of runtime and quality, performing particularly well for noisy and cluttered images encountered in the real world.

56 0

You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

Bowen Wen, Kostas E. Bekris, S. Schaal, Wenzhao Lian•Sat Jan 29 2022

This work proposes a novel, category-level manipulation framework that leverages an object-centric, categories-level representation and model-free 6 DoF motion tracking and allows to teach different manipulation strategies by solely providing a single demonstration, without complicated manual programming.

124 0

Uncertainty-Aware AB3DMOT by Variational 3D Object Detection

Illia Oleksiienko, A. Iosifidis•Sat Feb 11 2023

A Variational Neural Network-based TANet 3D object detector is proposed to generate 3D object detections with uncertainty and introduce these detections to an uncertainty-aware AB3DMOT tracker, and a method to initialize the Variational 3D object detector with a pretrained TANet model, which leads to the best performing models.

5 0

Dynamics based 3D skeletal hand tracking

L. Keselman, S. Melax, Sterling Orsten•Wed Mar 20 2013

This work enables real-time, robust 3D skeletal tracking of a user's hand, while utilizing a single x86 CPU core for processing, using an efficient physical simulation.

203 0

Variational Voxel Pseudo Image Tracking

Alexandros Iosifidis, Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis, A. Tefas•Sat Feb 11 2023

A Variational Neural Network-based version of a Voxel Pseudo Image Tracking method for 3D Single Object Tracking and it is shown that both methods improve tracking performance, while penalization of uncertain features provides the best uncertainty quality.

1 0

Adding a benchmark result helps the community track progress.

3D Object Tracking | State-of-the-Art