6 accepted papers
Mobile photography has made great strides in recent years. However, low light imaging still remains a challenge. Long exposures can improve signal-to-noise ratio (SNR) but undesirable motion blur can occur when capturing dynamic scenes. As a result, imaging pipelines often rely on computational phot…
We present a novel single-view surface normal estimation method that combines traditional line and vanishing point analysis with a deep learning approach. Starting from a color image and a Manhattan line map, we use a deep neural network to regress on a dense normal map, and a dense Manhattan label…
Understanding fashion styles and trends is of great potential interest to retailers and consumers alike. The photos people upload to social media are a historical and public data source of how people dress across the world and at different times. While we now have tools to automatically recognize th…
Noise is an inherent issue of low-light image capture, which is worsened on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in low-light situations is to increase the shutter time, allowing each photosite to integrate more light and decrease noise va…
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set of plane-sweep volumes and use the proposed DeepMVS network to predict high-quality disparity maps. The key contribution…
We propose a new method for turning an Internet-scale corpus of categorized images into a small set of human-interpretable discriminative visual elements using powerful tools based on deep learning. A key challenge with deep learning methods is generating human-interpretable models. To address this,…