Rigid Multiview Varieties
The multiview variety from computer vision is generalized to images by $n$ cameras of points linked by a distance constraint. The resulting five-dimensional variety lives in a product of $2n$ projective planes. We determine defining polynomial equations, and we explore generalizations of this variety to scenarios of interest in applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Algebra and Geometry of Camera Resectioning
We study algebraic varieties associated with the camera resectioning problem. We characterize these resectioning varieties' multigraded vanishing ideals using Gr\"obner basis techniques. As an application, we derive and …
Multiview Regenerative Morphing with Dual Flows
This paper aims to address a new task of image morphing under a multiview setting, which takes two sets of multiview images as the input and generates intermediate renderings that not only exhibit smooth transitions betw…
Image MorphingPatentNet: A Large-Scale Incomplete Multiview, Multimodal, Multilabel Industrial Goods Image Database
In deep learning area, large-scale image datasets bring a breakthrough in the success of object recognition and retrieval. Nowadays, as the embodiment of innovation, the diversity of the industrial goods is significantly…
BenchmarkingClusteringDiversityimage-classification+5Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification
Identifying the land cover category for each pixel in a hyperspectral image (HSI) relies on spectral and spatial information. An HSI cuboid with a specific patch size is utilized to extract spatial-spectral feature repre…
ClassificationDimensionality ReductionHyperspectral Image Classificationimage-classification+1Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation
We propose to tackle the problem of multiview 2D/3D rigid registration for intervention via a Point-Of-Interest Network for Tracking and Triangulation (POINT^2). POINT^2 learns to establish 2D point-to-point corresponden…