Approaching Visual Search in Photo-Realistic Scenes

Visual search is extended from the domain of polygonal figures presented on a uniform background to scenes in which search is for a photo-realistic object in a dense, naturalistic background. Scene generation for these displays relies on a powerful solid modeling program to define the three dimensional forms, surface properties, relative positions, and illumination of the objects and a rendering program to produce an image. Search in the presented experiments is for a rock with specific properties among other, similar rocks, although the method described can be generalized to other situations. Using this technique we explore the effects of illumination and shadows in aiding search for a rock in front of and closer to the viewer than other rocks in the scene. For these scenes, shadows of two different contrast levels can significantly deet·ease reaction times for displays in which target rocks are similar to distractor rocks. However, when the target rock is itself easily distinguishable from dis tractors on the basis of form, the presence or absence of shadows has no discernible effect. To relate our findings to those for earlier polygonal displays, we simplified the non-shadow displays so that only boundary information remained. For these simpler displays, search slopes (the reaction time as a function of the number of distractors) were significantly faster, indicating that the more complex photo-realistic objects require more time to process for visual search. In contrast with several previous experiments involving polygonal figures, we found no evidence for an effect of illumination direction on search times

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, most existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to existing approaches, while also supporting weaker supervision. Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to reason over lighting parameters and exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach in various settings, showing that: (i) it learns to disentangle shape from pose and lighting; (ii) using shading in the loss improves performance compared to just silhouettes; (iii) when using a standard single white light, our model outperforms state-of-the-art 2D-supervised methods, both with and without pose supervision, thanks to exploiting shading cues; (iv) performance improves further when using multiple coloured lights, even approaching that of state-of-the-art 3D-supervised methods; (v) shapes produced by our model capture smooth surfaces and fine details better than voxel-based approaches; and (vi) our approach supports concave classes such as bathtubs and sofas, which methods based on silhouettes cannot learn.Comment: Extension of arXiv:1807.09259, accepted to IJCV. Differentiable renderer available at https://github.com/pmh47/dir

Simulators, graphic

Includes bibliographical references (pages 1607-1608).There are many situations in which a computer simulation with a graphic display can be very useful in the design of a robotic system. First of all, when a robot is planned for an industrial application, there are many commercially available arms that can be selected. A graphics-based simulation would allow the manufacturing engineer to evaluate alternative choices quickly and easily. The engineer can also use such a simulation tool to design interactively the workcell in which the robot operates and integrate the robot with other systems, such as part feeders and conveyors with which it must closely work. Even before the workcell is assembled or the arm first arrives, the engineer can optimize the placement of the robot with respect to the fixtures it must reach and ensure that the arm is not blocked by supports. By being able to evaluate workcell designs off-line and away from the factory floor, changes can be made without hindering factory production and thus the net productivity of the design effort can be increased

Interactive display of isosurfaces with global illumination

Journal ArticleAbstract-In many applications, volumetric data sets are examined by displaying isosurfaces, surfaces where the data, or some function of the data, takes on a given value. Interactive applications typically use local lighting models to render such surfaces. This work introduces a method to precompute or lazily compute global illumination to improve interactive isosurface renderings. The precomputed illumination resides in a separate volume and includes direct light, shadows, and interreflections. Using this volume, interactive globally illuminated renderings of isosurfaces become feasible while still allowing dynamic manipulation of lighting, viewpoint and isovalue

What Is Around The Camera?

How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped and far-from-perfect mirror. An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance.Comment: Accepted to ICCV. Project: http://homes.esat.kuleuven.be/~sgeorgou/multinatillum