Pith. sign in

REVIEW 3 cited by

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1905.05425 v2 pith:I5XL23AM submitted 2019-05-14 cs.CV

Ruiqi Cheng , Kaiwei Wang , Shufei Lin , Weijian Hu , Kailun Yang , Xiao Huang , Huabing Li , Dongming Sun
show 1 more author
Jian Bai
This is my paper · ORCID
classification cs.CV
keywords annularpanoramicimageslocalizationvariationsdeepactivecamera
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Semantic Segmentation of Panoramic Images Using a Synthetic Dataset

    cs.CV 2019-09 reject novelty 5.0 of 10

    Panoramic training images cut from 360-degree synthetic scenes, especially 180-degree FoV views, improve semantic segmentation accuracy and distortion robustness compared with conventional training data.

  2. A Multimodal Vision Sensor for Autonomous Driving

    eess.IV 2019-08 conditional novelty 4.0 of 10

    The authors integrate three off-the-shelf camera types into one vehicle-mounted sensor and demonstrate cross-modal registration and water hazard detection without quantitative accuracy evaluation.

  3. See Clearer at Night: Towards Robust Nighttime Semantic Segmentation through Day-Night Image Conversion

    cs.CV 2019-08 conditional novelty 3.0 of 10

    Adding 2,000 CycleGAN-generated synthetic nighttime images to 5,000 real daytime BDD images lifts nighttime semantic segmentation mean IoU from 32.72% to 43.14% on the BDD night validation set.

Pith tools