For clarity, we split the work done into three areas that we investigate on this project
Capture
We were able to build camera systems (rigs) for capturing high dynamic range light fields of both small and large-size scenes. To overcome the limitations of the capture, we explored the existing methods for 3D scene acquisition, from the traditional multi-view 3D stereo to the recent learning-based methods that rely on multi-plane images or neural representations. Our initial investigation found that existing multi-view / light field methods, which do not attempt to recover 3D information, do not offer sufficient quality and data efficiency for our application. Therefore, we started using methods that either attempt to recover depth or rely on the existing depth information from other sources. We also found that the colour accuracy of the existing imaging pipelines is insufficient for our ultra-realistic display. To that end, we developed methods for more accurate high dynamic range merging.
Encoding
We have made substantial progress in terms of efficient encoding of visual content in three domains: temporal, luminance contrast and colour. We came up with a technique, called temporal resolution multiplexing (TRM), that allows displaying smooth motion at high frame-rates while rendering and encoding every second frame at half-the-resolution (
https://www.cl.cam.ac.uk/research/rainbow/projects/trm/(odnośnik otworzy się w nowym oknie)). This work has been awarded the Best IEEE VR Journal Paper Award in 2019. We also build a comprehensive model of the spatio-chromatic contrast sensitivity (
https://www.cl.cam.ac.uk/research/rainbow/projects/hdr-csf/(odnośnik otworzy się w nowym oknie)). We plan to use that model to derive an efficient colour representation for HDR data. We also developed machine-learning-based models for predicting visible differences in images, which offer much better prediction accuracy than existing techniques. Those models will be used to align the quality of visual encoding with the perceptual limitations.
Display
We have completed the construction of a high-dynamic-range multi-focal stereo (HDRMF) display, which delivers high brightness (4000 nit), deep blacks, high resolution, stereo disparity, and two focal planes for accommodation and defocus depth cues. Furthermore, the display has a see-through capability, so it is possible to see the displayed images on top of a real-world scene (like in AR displays) or to see the displayed image alone. The display is equipped with an eye-tracking camera, which can provide feedback on the position of the eyes. All this is combined with a real-time 3D rendering algorithm that can deliver images that match the appearance of real scenes.
Efficient perceptual measurements
Our work, to a large degree, relies on perceptual measurements. Since collecting perceptual data typically requires tedious psychophysical experiments, we devoted some effort to new machine-learning techniques to make such measurements as efficient and accurate as possible. For those purposes, we have developed a new active sampling method that lets us collect data in an optimal manner by sampling the points in our problem space that deliver the most information (
https://github.com/gfxdisp/asap(odnośnik otworzy się w nowym oknie)). The work received the Best Student Paper Award at ICPR 2020.
