Specular surface recovery from reflections of a planar pattern undergoing an unknown pure translation

LNCS v. 6493 entitled: Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part 2This paper addresses the problem of specular surface recovery, and proposes a novel solution based on observing the reflections of a translating planar pattern. Previous works have demonstrated that a specular surface can be recovered from the reflections of two calibrated planar patterns. In this paper, however, only one reference planar pattern is assumed to have been calibrated against a fixed camera observing the specular surface. Instead of introducing and calibrating a second pattern, the reference pattern is allowed to undergo an unknown pure translation, and a closed form solution is derived for recovering such a motion. Unlike previous methods which estimate the shape by directly triangulating the visual rays and reflection rays, a novel method based on computing the projections of the visual rays on the translating pattern is introduced. This produces a depth range for each pixel which also provides a measure of the accuracy of the estimation. The proposed approach enables a simple auto-calibration of the translating pattern, and data redundancy resulting from the translating pattern can improve both the robustness and accuracy of the shape estimation. Experimental results on both synthetic and real data are presented to demonstrate the effectiveness of the proposed approach. © 2011 Springer-Verlag Berlin Heidelberg.postprintThe 10th Asian Conference on Computer Vision, Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6493, p. 137-14

Sparkle Vision: Seeing the World through Random Specular Microfacets

In this paper, we study the problem of reproducing the world lighting from a single image of an object covered with random specular microfacets on the surface. We show that such reflectors can be interpreted as a randomized mapping from the lighting to the image. Such specular objects have very different optical properties from both diffuse surfaces and smooth specular objects like metals, so we design special imaging system to robustly and effectively photograph them. We present simple yet reliable algorithms to calibrate the proposed system and do the inference. We conduct experiments to verify the correctness of our model assumptions and prove the effectiveness of our pipeline

Object rigidity and reflectivity identification based on motion analysis

Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object. © 2010 IEEE

Rapid inference of object rigidity and reflectance using optic flow

Rigidity and reflectance are key object properties, important in their own rights, and they are key properties that stratify motion reconstruction algorithms. However, the inference of rigidity and reflectance are both difficult without additional information about the object's shape, the environment, or lighting. For humans, relative motions of object and observer provides rich information about object shape, rigidity, and reflectivity. We show that it is possible to detect rigid object motion for both specular and diffuse reflective surfaces using only optic flow, and that flow can distinguish specular and diffuse motion for rigid objects. Unlike nonrigid objects, optic flow fields for rigid moving surfaces are constrained by a global transformation, which can be detected using an optic flow matching procedure across time. In addition, using a Procrustes analysis of structure from motion reconstructed 3D points, we show how to classify specular from diffuse surfaces. © 2009 Springer Berlin Heidelberg

Radiance Scaling for Versatile Surface Enhancement

International audienceWe present a novel technique called Radiance Scaling for the depiction of surface shape through shading. It adjusts reflected light intensities in a way dependent on both surface curvature and material characteristics. As a result, diffuse shading or highlight variations become correlated to surface feature variations, enhancing surface concavities and convexities. This approach is more versatile compared to previous methods. First, it produces satisfying results with any kind of material: we demonstrate results obtained with Phong and Ashikmin BRDFs, Cartoon shading, sub-Lambertian materials, and perfectly reflective or refractive objects. Second, it imposes no restriction on lighting environment: it does not require a dense sampling of lighting directions and works even with a single light. Third, it makes it possible to enhance surface shape through the use of precomputed radiance data such as Ambient Occlusion, Prefiltered Environment Maps or Lit Spheres. Our novel approach works in real-time on modern graphics hardware and is faster than previous techniques

Indirect 3D Reconstruction Through Appearance Prediction

As humans, we easily perceive shape and depth, which helps us navigate our environment and interact with objects around us. Automating these abilities for computers is critical for many applications such as self-driving cars, augmented reality or architectural surveying. While active 3D reconstruction methods, such as laser scanning or structured light can produce very accurate results, they are typically expensive and their use cases can be limited. In contrast, passive methods that make use of only easily captured photographs, are typically less accurate as mapping from 2D images to 3D is an under-constrained problem. In this thesis we will focus on passive reconstruction techniques. We explore ways to get 3D shape from images in two challenging situations: 1) where a collection of images features a highly specular surface whose appearance changes drastically between the images, and 2) where only one input image is available. For both cases, we pose the reconstruction task as an indirect problem. In the first situation, the rapid change in appearance of highly specular objects makes it infeasible to directly establish correspondences between images. Instead, we develop an indirect approach using a panoramic image of the environment to simulate reflections, and recover the surface which best predicts the appearance of the object. In the second situation, the ambiguity inherent in single-view reconstruction is typically solved with machine learning, but acquiring depth data for training is both difficult and expensive. We present an indirect approach, where we train a neural network to regress depth by performing the proxy task of predicting the appearance of the image when the viewpoint changes. We prove that highly specular objects can be accurately reconstructed in uncontrolled environments, producing results that are 30% more accurate compared to the initialisation surface. For single frame depth estimation, our approach improves object boundaries in the reconstructions and significantly outperforms all previously published methods. In both situations, the proposed methods shrink the accuracy gap between camera-based reconstruction versus what is achievable through active sensors

Wahrnehmungsgrenzen kleiner Verformungen auf spiegelnden Oberflächen

Es werden zwei Modelle entwickelt und evaluiert, die Wahrnehmungsgrenzen kleiner Formabweichungen auf spiegelnden Oberflächen beschreiben. Beide Modelle berücksichtigen die gespiegelte Umgebung und führen die Wahrnehmungsgrenzen auf die Winkelauflösung des menschlichen Auges zurück. Ein weiteres Ziel dieser Arbeit ist die Erweiterung der Modelle auf Oberflächenrauheiten und -welligkeiten. Die vorhergesagten Wahrnehmungsgrenzen werden mit den Daten zweier Wahrnehmungsstudien verglichen

Proceedings of the 2015 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of proceedings of the talks given at the 2015 annual joint workshop of Fraunhofer IOSB and the Vision and Fusion Laboratory (IES) by the doctoral students of both institutions. The topics of individual contributions range from computer vision, optical metrology, and world modelling to data fusion and human-machine interaction

Automated Analysis of Time-resolved X-ray data using Optical Flow Methods

We develop a general-purpose framework for analysis of time-resolved X-ray data based on optical flow. We perform a systematic evaluation of state-of-the-art optical flow techniques and their components. On the top of motion estimation we provide an extensive data analysis toolkit. All the devised techniques can be applied in 4D (3D + time). The implementation employs advanced numerical schemes and computations on GPU. We present the application of the optical flow methods to a number of scientific problems from various research fields