Real-Time HDR Panorama Video

The interest for wide field of view panorama video is in-creasing. In this respect, we have an application that uses an array of cameras that overlook a soccer stadium. The input of these cameras are stitched together to provide a panoramic view of the stadium. One of the challenges we face is that large parts of the field are obscured by shad-ows on sunny days. Such circumstances cause unsatisfying video quality. We have therefore implemented and evaluated multiple algorithms related to high dynamic range (HDR) video. The evaluation shows that a combination of several approaches gives the most useful results in our scenario

Multi-Camera Platform for Panoramic Real-Time HDR Video Construction and Rendering

High dynamic range (HDR) images are usually obtained by capturing several images of the scene at different exposures. Previous HDR video techniques adopted the same principle by stacking HDR frames in time domain. We designed a new multi-camera platform which is able to construct and render HDR panoramic video in real-time, with 1024 × 256 resolution and a frame rate of 25 fps. We exploit the overlapping fields-of-view between the cameras with different exposures to create an HDR radiance map. We propose a method for HDR frame reconstruction which merges the previous HDR imaging techniques with the algorithms for panorama reconstruction. The developed FPGA-based processing system is able to reconstruct the HDR frame using the proposed method and tone map the resulting image using a hardware-adapted global operator. The measured throughput of the system is 245 MB/s, which is, up to our knowledge, among the fastest HDR video processing systems

Matterport3D: Learning from RGB-D Data in Indoor Environments

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification

Tone mapping HDR panoramas for viewing in Head Mounted Displays

Head-mounted displays enable a user to view a complete environment as if he/she was there; providing an immersive experience. However, the lighting in a full environment can vary significantly. Panoramic images captured with conventional, Low Dynamic Range (LDR), imaging of scenes with a large range of lighting conditions, can include areas of under- or over-exposed pixels. High Dynamic Range (HDR) imaging, on the other hand, is able to capture the full range of detail in a scene. However, HMDs are not currently HDR and thus the HDR panorama needs to be tone mapped before it can be displayed on the LDR HMD. While a large number of tone mapping operators have been proposed in the last 25 years, these were not designed for panoramic images, or for use with HMDs. This paper undertakes a two part subjective study to investigate which of the current, state-of-the-art tone mappers is most suitable for use with HMD