Kyu-Yul Lee and Jae-Young Sim, “Warping Residual Based Image Stitching for Large Parallax”. Kyu-Yul, Congratulations!
We publicly release a new UNIST LS3DPC dataset used in our papers. The associated paper is accecpted to IEEE Transactions on Pattern Analysis and Machine Intelligence. The dataset can be downloaded here.
There was a seminar titled “Graph Convolution” by Se-Won Jeong. Feb. 13, 2020.
There was a seminar titled “Few Shot Learning” by Eunpil Park. Feb. 6, 2020.
The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose an optimization problem to remove reflection automatically from multiple glass images taken at slightly different camera locations. We first warp the multiple glass images to a reference image, where the gradients are consistent in the transmission images while the gradients are varying across the reflection images. Based on this observation, we compute a gradient reliability such that the pixels belonging to the salient edges of the transmission image are assigned high reliability, but that of the reflection images are assigned low values. Then we suppress the gradients of the reflection images and recover the gradients of the transmission images only, by solving the proposed optimization problem in gradient domain. We reconstruct an original transmission image using the resulting optimal gradient map. Experimental results show that the proposed algorithm removes the reflection artifacts from glass images faithfully and outperforms the existing algorithms.
There was a seminar titled “Person Search” by KuHyeun Ko. Jan. 30, 2020.
The UNIST Large-Scale 3D Point Clouds (LS3DPC) dataset for virtual point removal consists of 11 large-scale point clouds containing several millions of points with XYZ cartesian coordinates and RGB colors. This UNIST LS3DPC dataset is captured by RIEGL VZ-400 terrestrial LiDAR scanner and Nikon D700 digital camera concurrently. The XYZ cartesian coordinates are measured in meters.