Non-Planar Inside-Out Dense Light-Field Dataset and Reconstruction Pipeline

Abstract

Light-field imaging provides full spatio-angular information of the real world by capturing the light rays in various directions. This allows image processing algorithms to result in immersive user experiences such as VR. To evaluate, and develop reconstruction algorithms, a precise and dense light-field dataset of the real world that can be used as ground truth is desirable. In this paper, a nonplanar capture is done and a view rendering pipeline is implemented. The acquired dataset includes two scenes that are captured by an accurate industrial robot with an attached color camera such that the camera is looking outward. The arm moves on a cylindrical path for a field of view of 125 degrees with angular step size of 0.01 degrees. Both scenes and their corresponding geometric calibration parameters will be available with the publication of the paper. The images are pre-processed in different steps. The disparity between two adjacent views with resolution of 5168𝑥3448 is less than 1.6 pixels; the parallax between the foreground and the background objects is less than 0.6 pixels. Furthermore, the pre-processed data is used for a view rendering experiment to demonstrate an exemplary use case. In addition, the rendered results are evaluated visually and objectively.

The Good News, the Bad News, and the Ugly Truth: A Review on the 3D Interaction of Light Field Displays

“…In the context of light field visualization, subjective quality evaluation commonly accompanies research efforts related to compression [81][82][83][84][85][86][87][88]. Other topics addressing light field QoE include, but are definitely not limited to, objective quality assessment (i.e., the prediction or estimation of subjectively perceived quality through metrics and models) [89][90][91][92][93][94]; datasets for objective and subjective assessment [95][96][97][98][99][100][101][102][103]; reconstruction, subsampling, interpolation, and view synthesis [104][105][106][107][108][109][110]; spatial and angular resolution [111][112][113][114]; methodology and viewing conditions [115][116][117][118][119][120]; human factors and content impact [121][122][123][124]; and rendering [125]…”

KULF-TT53: A Display-Specific Turntable-Based Light Field Dataset for Subjective Quality Assessment

“…Numerous static-scene datasets [17,[20][21][22][23] were captured by using the Lytro Illum plenoptic camera, not including motion or video content among them. Other static-content light field datasets [15,[24][25][26][27] were captured by using DSLR cameras without incorporating video elements. A high-angularresolution dataset consisting of seven objects is introduced by the work of Tamboli et al [24].…”