An unsupervised method detects vanishing points by fitting implicit lines through recurring feature correspondences and combining them with explicit lines via weighted RANSAC, outperforming classical and supervised methods on its new recurring-pattern benchmarks.
Novel 3D Scene Understanding Applications From Recurrence in a Single Image
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We demonstrate the utility of recurring pattern discovery from a single image for spatial understanding of a 3D scene in terms of (1) vanishing point detection, (2) hypothesizing 3D translation symmetry and (3) counting the number of RP instances in the image. Furthermore, we illustrate the feasibility of leveraging RP discovery output to form a more precise, quantitative text description of the scene. Our quantitative evaluations on a new 1K+ Recurring Pattern (RP) benchmark with diverse variations show that visual perception of recurrence from one single view leads to scene understanding outcomes that are as good as or better than existing supervised methods and/or unsupervised methods that use millions of images.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Recurrence-based Vanishing Point Detection
An unsupervised method detects vanishing points by fitting implicit lines through recurring feature correspondences and combining them with explicit lines via weighted RANSAC, outperforming classical and supervised methods on its new recurring-pattern benchmarks.