Chao Liu

PhD Student

I am a PhD student in Robotics Institute at Carnegie Mellon University. Before coming to CMU, I received Master degree from Imaging Science Department at Rochester Institute of Technology working with Jinwei Gu and Bachelor degree from University of Electronic Science and Technology of China. I am advised by Srinivasa Narasimhan and Artur Dubrawski. My research interest is in computational photography, computer vision and medical imaging.

Research

We propose a high spatial resolution Diffuse Optical Tomography (DOT) system that can detect accurate boundaries and relative depth of heterogeneous structures up to a depth of 8mm below highly scattering medium

We propose a deep learning method to estimate per-pixel depth and its uncertainty continuously from a monocular video stream, with the goal of effectively turning an RGB camera into an RGB-D camera. Unlike prior DL-based methods, we estimate a depth probability distribution for each pixel rather than a single depth value, leading to an estimate of a 3D depth probability volume for each input frame. These depth probability volumes are accumulated over time under a Bayesian filtering framework as more incoming frames are processed sequentially, which effectively reduces depth uncertainty and improves accuracy, robustness, and temporal stability.

In this work, we propose a two-stage near-light photometric stereo method using circularly placed point light sources (commonly seen in recent consumer imaging devices like NESTcam, Amazon Cloudcam, etc). Because of the small light source baseline, the change of image intensity for the input image is small. In addition, in the near-light condition, the distant light assumption fails. So the light directions and intensities are not evenly distributed across the scene. Our proposed method tackles both issues.

Thin structures such as fences, grass and vessels are common in photography and scientific imaging. They contribute complexity to 3D scenes with sharp depth variations/discontinuities and mutual occlusions. In this work, we develop a method to estimate the occlusion matte and depths of thin structures from a focal image stack, which is obtained either by varying the focus/aperture of the lens or computed from a one-shot light field image.

We develop real time tools to capture and analyze blood circulation in micro vessels that is important for critical care applications. The tools provide highly detailed blood flow statistics for bedside and surgical care.

We present a computational imaging method for raw material classification using features of Bidirectional Texture Functions (BTF). We proposed to learn discriminative illumination patterns and texture filters, with which we can directly measure optimal projections of BTFs for classification.

We propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns---which we call ``discriminative illumination"---are learned from training samples, after projecting to which, the spectral reflectance of different materials are maximally separated.

Publications