3D Hallway Modeling Using a Single Image

Real-time, low-resource corridor reconstruction using a single consumer grade RGB camera is a powerful tool for allowing a fast, inexpensive solution to indoor mobility of a visually impaired person or a robot. The perspective and known geometry of a corridor is used to extract the important features of the image and create a 3D model from a single image. Multiple 3D models can be combined to increase confidence and provide a global 3D model. This paper presents our results on 3D corridor modeling using single images. First a simple but effective 3D corridor modeling approach is introduced which makes very few assumptions of the camera information. Second, a perspective based Hough transform algorithm is proposed to detect vertical lines in order to determine the edges of the corridor. Finally, issues in real-time implementation on a smartphone are discussed. Experimental results are provided to validate the proposed approach. Index Terms-- indoor modeling, vanishing point, visual impairment, perspective based Hough transform

Semi-supervised Regression with Generative Adversarial Networks Using Minimal Labeled Data

This work studies the generalization of semi-supervised generative adversarial networks (GANs) to regression tasks. A novel feature layer contrasting optimization function, in conjunction with a feature matching optimization, allows the adversarial network to learn from unannotated data and thereby reduce the number of labels required to train a predictive network. An analysis of simulated training conditions is performed to explore the capabilities and limitations of the method. In concert with the semi-supervised regression GANs, an improved label topology and upsampling technique for multi-target regression tasks are shown to reduce data requirements. Improvements are demonstrated on a wide variety of vision tasks, including dense crowd counting, age estimation, and automotive steering angle prediction. With training data limitations arguably being the most restrictive component of deep learning, methods which reduce data requirements hold immense value. The methods proposed here are general-purpose and can be incorporated into existing network architectures with little or no modifications to the existing structure