This paper presents a photometric stereo method based on deep learning. One of the major difficulties in photometric stereo is designing can appropriate reflectance model that is both capable of representing real-world reflectances and computationally tractable in terms of deriving surface normal. Unlike previous photometric stereo methods that rely on a simplified parametric image formation model, such as the Lambert's model, the proposed method aims at establishing a flexible mapping between complex reflectance observations and surface normal by the use of a deep neural network. As a result we propose a deep photometric stereo network (DPSN) that takes reflectance observations under varying light directions and infers the corresponding surface normal per pixel. To make the DPSN applicable to real-world objects, a database of measured bidirectional reflectance distribution functions (MERL BRDF database) has been used for training the network. Evaluation using simulation and real-world scenes shows effectiveness of the proposed approach over previous techniques.

This paper presents a photometric stereo method based on deep learning. One of the major difficulties in photometric stereo is designing an appropriate reflectance model that is both capable of representing real-world reflectances and computationally tractable for deriving surface normal. Unlike previous photometric stereo methods that rely on a simplified parametric image formation model, such as the Lambert's model, the proposed method aims at establishing a flexible mapping between complex reflectance observations and surface normal using a deep neural network. In addition, the proposed method predicts the reflectance, which allows us to understand surface materials and to render the scene under arbitrary lighting conditions. As a result, we propose a deep photometric stereo network (DPSN) that takes reflectance observations under varying light directions and infers the surface normal and reflectance in a per-pixel manner. To make the DPSN applicable to real-world scenes, a dataset of measured BRDFs (MERL BRDF dataset) has been used for training the network. Evaluation using simulation and real-world scenes shows the effectiveness of the proposed approach in estimating both surface normal and reflectances.

Indoor positioning is a core technology for indoor daily life applications such as elderly care and home automation. This study presents a device-free and privacy preserving indoor positioning method using infrared (IR) cameras and retroreflectors. The proposed method uses IR cameras equipped with IR LEDs to capture retroreflections from markers attached to walls in the environment, and detects a person who passes between the camera and a marker by observing the occlusion. Because our method employs occlusion of markers, it can track a person without attaching tags to the person. Also, our camera device permits us to filter out visible light and thus the appearance of the person is not recorded. Our evaluation in real environments showed that our method achieved an average positioning error of about 0.3 meters.

We present a method for geometric point light source calibration. Unlike prior works that use Lambertian spheres, mirror spheres, or mirror planes, we use a calibration target consisting of a plane and small shadow casters at unknown positions above the plane. We show that shadow observations from a moving calibration target under a fixed light follow the principles of pinhole camera geometry and epipolar geometry, allowing joint recovery of the light position and 3D shadow caster positions, equivalent to how conventional structure from motion jointly recovers camera parameters and 3D feature positions from observed 2D features. Moreover, we devised a unified light model that works with nearby point lights as well as distant light in one common framework. Our evaluation shows that our method yields light estimates that are stable and more accurate than existing techniques while having a much simpler setup and requiring less manual labor.