Proceedings ArticleDOI
Multiple 3D Object tracking for augmented reality
Youngmin Park,Vincent Lepetit,Woontack Woo +2 more
- pp 117-120
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TLDR
This work presents a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time in order to take the advantages of the two approaches to object detection and tracking.Abstract:
We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well with the number of objects, and a proper way to deal with several objects is required. Our method combines object detection and tracking: Frame-to-frame tracking is less computationally demanding but is prone to fail, while detection is more robust but slower. We show how to combine them to take the advantages of the two approaches, and demonstrate our method on several real sequences.read more
Citations
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Proceedings ArticleDOI
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou,Oncel Tuzel +1 more
TL;DR: Zhou et al. as mentioned in this paper propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network.
Posted Content
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou,Oncel Tuzel +1 more
TL;DR: VoxelNet is proposed, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network and learns an effective discriminative representation of objects with various geometries, leading to encouraging results in3D detection of pedestrians and cyclists.
Proceedings ArticleDOI
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
TL;DR: Wang et al. as discussed by the authors proposed a two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD), which uses raw point clouds as input to generate accurate proposals by seeding each point with a new spherical anchor.
Journal ArticleDOI
Pose Estimation for Augmented Reality: A Hands-On Survey
TL;DR: This paper aims at presenting a brief but almost self-contented introduction to the most important approaches dedicated to vision-based camera localization along with a survey of several extension proposed in the recent years.
Journal ArticleDOI
Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking
TL;DR: This work presents a carefully designed dataset of video sequences of planar textures with ground truth, which includes various geometric changes, lighting conditions, and levels of motion blur, and presents a comprehensive quantitative evaluation of detector-descriptor-based visual camera tracking based on this testbed.
References
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Book ChapterDOI
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Proceedings ArticleDOI
Parallel Tracking and Mapping for Small AR Workspaces
Georg Klein,David W. Murray +1 more
TL;DR: A system specifically designed to track a hand-held camera in a small AR workspace, processed in parallel threads on a dual-core computer, that produces detailed maps with thousands of landmarks which can be tracked at frame-rate with accuracy and robustness rivalling that of state-of-the-art model-based systems.
Proceedings ArticleDOI
Scalable Recognition with a Vocabulary Tree
David Nister,Henrik Stewenius +1 more
TL;DR: A recognition scheme that scales efficiently to a large number of objects and allows a larger and more discriminatory vocabulary to be used efficiently is presented, which it is shown experimentally leads to a dramatic improvement in retrieval quality.
Journal ArticleDOI
An algorithm for tracking multiple targets
TL;DR: An algorithm for tracking multiple targets in a cluttered environment is developed, capable of initiating tracks, accounting for false or missing reports, and processing sets of dependent reports.