Minifying photometric stereo via knowledge distillation-based feature translation.

Photometric stereo (PS) estimates the surface normals of an object by utilizing multiple images captured under different light conditions. To obtain accurate surface normals, a large number of input images is often required. Therefore, a huge effort is needed to capture images and calibrate light directions together with a heavy computational cost.

Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation.

Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs. In this paper, we propose a lightweight depth completion network for depth perception in real-world environments.

Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes.

We propose a deep convolutional neural network (CNN) method for natural image matting. Our method takes multiple initial alpha mattes of the previous methods and normalized RGB color images as inputs, and directly learns an end-to-end mapping between the inputs and reconstructed alpha mattes. Among the various existing methods, we focus on using two simple methods as initial alpha mattes: the closed-form matting and KNN matting.

Semi-Calibrated Photometric Stereo.

While conventional calibrated photometric stereo methods assume that light intensities and sensor exposures are known or unknown but identical across observed images, this assumption easily breaks down in practical settings due to individual light bulb's characteristics and limited control over sensors. This paper studies the effect of unknown and possibly non-uniform light intensities and sensor exposures among observed images on the shape recovery based on photometric stereo. This leads to the development of a "semi-calibrated" photometric stereo method, where the light directions are known but light intensities (and sensor exposures) are unknown.

Automatic Trimap Generation and Consistent Matting for Light-Field Images.

In this paper, we introduce an automatic approach to generate trimaps and consistent alpha mattes of foreground objects in a light-field image. Our method first performs binary segmentation to roughly segment a light-field image into foreground and background based on depth and color. Next, we estimate accurate trimaps through analyzing color distribution along the boundary of the segmentation using guided image filter and KL-divergence.