Combining approximate geometry with view-dependent texture mapping - a hybrid approach to 3D video teleconferencing

Kurashima, Celso Setsuo; Yang, Ruigang; Lastra, Anselmo

doi:10.1109/sibgra.2002.1167133

Cited by 4 publications

(3 citation statements)

References 25 publications

(21 reference statements)

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“…This approach can take care of occluded or invisible regions, as long as they are visible in at least one of the chosen views. More sophisticated approaches were presented in [10] and [14] by determining the three best views for each individual polygon and combining them with per-vertex blending weights. The best views were chosen based only on the angle deviation between virtual and original cameras.…”

Section: Improved Renderingmentioning

confidence: 99%

A System for Distributed Multi-camera Capture and Processing

Easterbrook¹,

Grau²,

Schübel³

2010

2010 Conference on Visual Media Production

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This contribution describes a distributed multi-camera capture and processing system for real-time media production applications. Its main design purpose is to allow prototyping of distributed processing algorithms for free-viewpoint applications, but the concept can be adapted to other (multicamera) applications.The system integrates broadcast components into a distributed IT-based processing system. The problems that are addressed in this contribution are the synchronisation of these sub-systems and the data and control flow.For the synchronisation of the broadcast and IT components we developed a time stamp mechanism based on tightly synchronised PC clocks. This time is used as the master clock of the system. The video frames from the gen-locked multi-camera streams are implicitly synchronised by adjusting their relative timing against this system clock. For the purpose of synchronising the PC clocks two protocols (NTP and PTP) were evaluated. The second main contribution is the processing of multi-camera data in the distributed system. For this purpose a software framework was developed based on a distributed concurrency system implemented in Python.

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Section: Improved Renderingmentioning

confidence: 99%

A System for Distributed Multi-camera Capture and Processing

Easterbrook¹,

Grau²,

Schübel³

2010

2010 Conference on Visual Media Production

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“…There are other popular IBR methods requiring different amount of inputs [11], [15], [17]. Unstructured lumigraph [4] attempts to generalize these methods by accepting varying amount of inputs (either a small number of reference images with geometric information or a large amount of reference images).…”

Section: Light Field Renderingmentioning

confidence: 99%

Image registration for image-based rendering

Siu

Lau

2005

IEEE Trans. on Image Process.

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Abstract-Image-based rendering (IBR) has received much attention in recent years for its ability to synthesize photo-realistic novel views. To support translational motion, existing IBR methods either require a large amount of reference images or assume that some geometric information is available. However, rendering with a large amount of images is very expensive in terms of image acquisition, data storage, and memory costs. As IBR accepts various kinds of geometric proxy, we may use image registration techniques, such as stereo matching and structure and motion recognition, to obtain geometric information to help reduce the number of images required. Unfortunately, existing image registration techniques only support a small search range and require closely sampled reference images. This results in a high spatial sampling rate, making IBR impractical for use in scalable walkthrough environments.Our primary objective of this project is to develop an image registration technique that would recover the geometric proxy for IBR while, at the same time, reducing the number of reference images required. In this paper, we analyze the roles and requirements of an image registration technique for reducing the spatial sampling rate. Based on these requirements, we present a novel image registration technique to automatically recover the geometric proxy from reference images. With the distinguishing feature of supporting a large search range, the new method can accurately identify correspondences even though the reference images may only be sparsely sampled. This can significantly reduce the acquisition effort, the model size, and the memory cost.Index Terms-Image-based rendering (IBR), image matching, image registration, object recognition.

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“…Several attempts have been made to address this problem using a dense reconstruction of the surrounding real scenery through depth fusion [11][12][13] or provide viewdependent disparity information based on the depth map of the scenery [8,14,15]. These techniques require a structured set of patch images in an array for mosaic-based imaging as inputs.…”

Section: Introductionmentioning

confidence: 99%

Camera pose estimation framework for array‐structured images

et al. 2022

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Despite the significant progress in camera pose estimation and structure‐from‐motion reconstruction from unstructured images, methods that exploit a priori information on camera arrangements have been overlooked. Conventional state‐of‐the‐art methods do not exploit the geometric structure to recover accurate camera poses from a set of patch images in an array for mosaic‐based imaging that creates a wide field‐of‐view image by sewing together a collection of regular images. We propose a camera pose estimation framework that exploits the array‐structured image settings in each incremental reconstruction step. It consists of the two‐way registration, the 3D point outlier elimination and the bundle adjustment with a constraint term for consistent rotation vectors to reduce reprojection errors during optimization. We demonstrate that by using individual images' connected structures at different camera pose estimation steps, we can estimate camera poses more accurately from all structured mosaic‐based image sets, including omnidirectional scenes.

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Combining approximate geometry with view-dependent texture mapping - a hybrid approach to 3D video teleconferencing

Cited by 4 publications

References 25 publications

A System for Distributed Multi-camera Capture and Processing

A System for Distributed Multi-camera Capture and Processing

Image registration for image-based rendering

Camera pose estimation framework for array‐structured images

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