Computational Imaging Techniques to Recover Omni3D Structure & Surface Properties
The process of imaging converts a 3D world into a 2D image. This process is inherently lossy, making the problem of understanding the world, ill-posed. During the imaging process, a light ray emanating from a source bounces of different surfaces, interacting with them according to the surface properties (albedo, color, specularity) before reaching the camera. The structure that we see in an image is primarily based on the final surface from which the light was reflected, except when that surface is highly specular (mirror-like). In this work, we explore imaging techniques the recover both the structure and surface properties of a scene. For structure recovery, we extend the idea of stereo imaging and present a practical solution to capture a complete 360◦ panorama using a single camera. Current approaches either use a moving camera for capturing multiple images of a scene, which are then stitched together to form the final panorama, or use multiple cameras that are synchronized. A moving camera limits the solution to static scenes, while multi-camera solutions require dedicated calibrated setups. Our approach improves upon the existing solutions in two significant ways: It solves the problem using a single camera, thus minimizing the calibration problem and providing us the ability to convert any digital camera into a stereo panoramic capture device. It captures all the light rays required for stereo panoramas in a single frame using a compact custom designed mirror, thus making the design practical to manufacture and easier to use. We analyze the optimality of the design as well as present panoramic stereo and depth estimation results. The methods for structure recovery, including stereo are often fooled when the surface is highly specular. To alleviate this, we propose an active-illumination based method to detect and segment mirror-like surfaces in a scene. In computer vision, many active illumination techniques employ Projector-Camera systems to extract useful information from the scenes. Known illumination patterns are projected onto the scene and their deformations in the captured images are then analyzed. We observe that the local frequencies in the captured pattern for the mirror-like surfaces is different from the projected pattern. This property allows us to design a custom Projector-Camera system to segment mirror-like surfaces by analyzing the local frequencies in the captured images. The system projects a sinusoidal pattern and capture the images from projector’s point of view. We present segmentation results for the scenes including multiple reflections and inter-reflections from the mirror-like surfaces. The method can further be used in the separation of direct and global components for the mirror-like surfaces by illuminating the non-mirror-like objects separately. We show how our method is also useful for accurate estimation of shape of the non-mirror-like regions in the presence of mirror-like regions in a scene.
|Year of completion:||November 2019|
|Advisor :||Anoop M Namboodiri|