Depth-Map Generation using Pixel Matching in Stereoscopic Pair of Images
Modern day multimedia content generation and dissemination is moving towards the presentation of more and more `realistic' scenarios. The switch from 2-dimensional (2D) to 3-dimensional (3D) has been a major driving force in that direction. Over the recent past, a large number of approaches have been proposed for creating 3D images/videos most of which are based on the generation of depth-maps. This paper presents a new algorithm for obtaining depth information pertaining to a depicted scene from a set of available pair of stereoscopic images. The proposed algorithm performs a pixel-to-pixel matching of the two images in the stereo pair for estimation of depth. It is shown that the obtained depth-maps show improvements over the reported counterparts.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Depth EstimationSimilar Papers 제목 키워드 기반
Learning Single Camera Depth Estimation using Dual-Pixels
Deep learning techniques have enabled rapid progress in monocular depth estimation, but their quality is limited by the ill-posed nature of the problem and the scarcity of high quality datasets. We estimate depth from a …
Depth EstimationMonocular Depth EstimationMCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source
A new stereoscopic image quality assessment database rendered using the 2D-image-plus-depth source, called MCL-3D, is described and the performance benchmarking of several known 2D and 3D image quality metrics using the …
BenchmarkingImage Quality AssessmentStereoscopic image quality assessmentQuality assessment of image matchers for DSM generation -- a comparative study based on UAV images
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need …
3D Surface GenerationStereoscopic Depth Perception Through Foliage
Both humans and computational methods struggle to discriminate the depths of objects hidden beneath foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing …
Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
Monocular-to-stereo conversion synthesizes stereoscopic content from 2D videos for immersive 3D experiences. In modern Depth-Image-Based Rendering (DIBR) approaches, stereo inpainting of disocclusions is the critical bot…
Self-Supervised LearningVideo Generation