Multimodal Visual Data Registration for Web-Based Visualization in Media Production

Abstract

Recent developments of video and sensing technology have led to large volumes of digital media data. Current media production relies on videos from the principal camera together with a wide variety of heterogeneous source of supporting data [photos, light detection and ranging point clouds, witness video camera, high dynamic range imaging, and depth imagery]. Registration of visual data acquired from various 2D and 3D sensing modalities is challenging because current matching and registration methods are not appropriate due to differences in structure, format, and noise characteristics for multimodal data. A combined 2D/3D visualization of this registered data allows an integrated overview of the entire data set. For such a visualization, a Web-based context presents several advantages. In this paper, we propose a unified framework for registration and visualization of this type of visual media data. A new feature description and matching method is proposed, adaptively considering local geometry, semiglobal geometry, and color information in the scene for more robust registration. The resulting registered 2D/3D multimodal visual data are too large to be downloaded and viewed directly via the Web browser, while maintaining an acceptable user experience. Thus, we employ hierarchical techniques for compression and restructuring to enable efficient transmission and visualization over the Web, leading to interactive visualization as registered point clouds, 2D images, and videos in the browser, improving on the current state-of-the-art techniques for Web-based visualization of big media data. This is the first unified 3D Web-based visualization of multimodal visual media production data sets. The proposed pipeline is tested on big multimodal data set typical of film and broadcast production, which are made publicly available. The proposed feature description method shows two times higher precision of feature matching and more stable registration performance than existing 3D feature descriptors.