A Possible Predictor of Visual Discomfort of Viewing Stereoscopic 3D Maps: The Imbalance of Disparity Distributions

Stereoscopic 3D (S3D) maps provide an accurate 3D representation of terrain texture for the precise perception of Earth’s surface. Visual discomfort on S3D images primarily comes from accommodation-vergence conflict, which is related to disparity (the distance between two corresponding points in the left and right stereo images). Previous studies have identified that disparity characteristics are related to visual discomfort. However, the relation between disparity characteristics and visual discomfort has not been investigated in orthographic S3D maps. It is unknown whether disparity characteristics are good indicators of visual discomfort regarding S3D maps. This study proposed a new visual discomfort predictor and compared it to the disparity characteristics already existing in the IEEE standard 3333.1.1™-2015. The comparisons indicate that the imbalance index can be a good predictor of visual discomfort regarding S3D maps. The predictor will be used in a personalized computational model to predict visual discomfort.

Semi-Automatic Depth Map Generation In Unconstrained Images And Video Sequences For 2D To Stereoscopic 3D Conversion

In this work, we describe a system for accurately estimating depth through synthetic depth maps in unconstrained conventional monocular images and video sequences, to semi-automatically convert these into their stereoscopic 3D counterparts. With current accepted industry efforts, this conversion process is performed automatically in a black box fashion, or manually converted using human operators to extract features and objects on a frame by frame basis, known as rotoscopers. Automatic conversion is the least labour intensive, but allows little to no user intervention, and error correction can be difficult. Manual is the most accurate, providing the most control, but very time consuming, and is prohibitive for use to all but the largest production studios. Noting the merits and disadvantages between these two methods, a semi-automatic method blends the two together, allowing for faster and accurate conversion, while decreasing time for releasing 3D content for user digest. Semi-automatic methods require the user to place user-defined strokes over the image, or over several keyframes in the case of video, corresponding to a rough estimate of the depths in the scene at these strokes. After, the rest of the depths are determined, creating depth maps to generate stereoscopic 3D content, and Depth Image Based Rendering is employed to generate the artificial views. Here, depth map estimation can be considered as a multi-label image segmentation problem: each class is a depth value. Additionally, for video, we allow the option of labeling only the first frame, and the strokes are propagated using one of two techniques: A modified computer vision object tracking algorithm, and edge-aware temporally consistent optical flow./p pFundamentally, this work combines the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion of depths, with smooth gradients from Random Walks, combined with the edge preserving properties from Graph Cuts can create the best possible result. To demonstrate that the proposed framework generates good quality stereoscopic content with minimal effort, we create results and compare to the current best known semi-automatic conversion framework. We also show that our results are more suitable for human perception in comparison to this framework.

Semi-Automatic Depth Map Generation In Unconstrained Images And Video Sequences For 2D To Stereoscopic 3D Conversion

In this work, we describe a system for accurately estimating depth through synthetic depth maps in unconstrained conventional monocular images and video sequences, to semi-automatically convert these into their stereoscopic 3D counterparts. With current accepted industry efforts, this conversion process is performed automatically in a black box fashion, or manually converted using human operators to extract features and objects on a frame by frame basis, known as rotoscopers. Automatic conversion is the least labour intensive, but allows little to no user intervention, and error correction can be difficult. Manual is the most accurate, providing the most control, but very time consuming, and is prohibitive for use to all but the largest production studios. Noting the merits and disadvantages between these two methods, a semi-automatic method blends the two together, allowing for faster and accurate conversion, while decreasing time for releasing 3D content for user digest. Semi-automatic methods require the user to place user-defined strokes over the image, or over several keyframes in the case of video, corresponding to a rough estimate of the depths in the scene at these strokes. After, the rest of the depths are determined, creating depth maps to generate stereoscopic 3D content, and Depth Image Based Rendering is employed to generate the artificial views. Here, depth map estimation can be considered as a multi-label image segmentation problem: each class is a depth value. Additionally, for video, we allow the option of labeling only the first frame, and the strokes are propagated using one of two techniques: A modified computer vision object tracking algorithm, and edge-aware temporally consistent optical flow./p pFundamentally, this work combines the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion of depths, with smooth gradients from Random Walks, combined with the edge preserving properties from Graph Cuts can create the best possible result. To demonstrate that the proposed framework generates good quality stereoscopic content with minimal effort, we create results and compare to the current best known semi-automatic conversion framework. We also show that our results are more suitable for human perception in comparison to this framework.

Sourcing and Qualifying Passive Polarised 3D TVs

Millions of Stereoscopic 3D capable TVs were sold into the consumer market from 2007 through to 2016. A wide range of display technologies were supported including rear-projection DLP, Plasma, LCD and OLED. Some displays supported the Active 3D method using liquid-crystal shutter glasses, and some displays supported the Passive 3D method using circularly polarised 3D glasses. Displays supporting Full-HD and Ultra-HD (4K) resolution were available in sizes ranging from 32" to 86" diagonal. Unfortunately display manufacturers eventually changed their focus to promoting other display technologies and 2016 was the last year that new 3D TVs were made for the consumer market. Fortunately, there are still millions of 3D displays available through the secondhand- market, however it can be difficult to know which displays have 3D display support. This paper will provide a listing of specifically Passive 3D TVs manufactured by LG, however it has been our experience that the 3D quality varied considerably from one display to another hence it is necessary to qualify the quality of the 3D available on these displays using a testing technique that will be described in the paper.