The Starry Night Texture

From a modern Bayesian point of view, the classic Julesz random-dot stereogram is a cue-conflict stimulus: texture cues specify an unbroken, unslanted surface, in conflict with any variation in depth specified by binocular disparity. We introduce a new visual stimulus based on a novel texture, the Starry Night Texture (SNT), that is incapable of conveying slant, depth edges, or texture boundaries, in a single view. Changing density and changing intensity are equivalent for SNT, so an instance of the texture is characterized (up to the random locations of the texture elements) by its densintensity. We describe the SNT in its ideal form, consider deviations from the ideal that are needed to realize the texture in practice, and describe a physical device that approximates SNT using backlit metal foil. In three experiments with computer-generated stimuli we examined human perception of SNT, to show that (1) the deviations from ideal that were needed to realize SNT do not affect the invariance of its appearance, across changes in distance of several orders of magnitude; (2) as predicted, observers match SNT better than other textures across changes in distance; and (3) the use of SNT in a slant perception experiment did not significantly increase observers\u27 reliance on stereoscopic slant cues, as compared to the sparse random dot displays that have been commonly employed to study human perception of shape from binocular disparity and motion

Stereo-Based Environment Scanning for Immersive Telepresence

The processing power and network bandwidth required for true immersive telepresence applications are only now beginning to be available. We draw from our experience developing stereo based tele-immersion prototypes to present the main issues arising when building these systems. Tele-immersion is a new medium that enables a user to share a virtual space with remote participants. The user is immersed in a rendered three-dimensional (3-D) world that is transmitted from a remote site. To acquire this 3-D description, we apply binocular and trinocular stereo techniques which provide a view-independent scene description. Slow processing cycles or long network latencies interfere with the users\u27 ability to communicate, so the dense stereo range data must be computed and transmitted at high frame rates. Moreover, reconstructed 3-D views of the remote scene must be as accurate as possible to achieve a sense of presence. We address both issues of speed and accuracy using a variety of techniques including the power of supercomputing clusters and a method for combining motion and stereo in order to increase speed and robustness. We present the latest prototype acquiring a room-size environment in real time using a supercomputing cluster, and we discuss its strengths and current weaknesses

T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.Comment: WACV 201

Advances in 3DTV: Theory and Practice

n/

A Representation Protocol for Traditional Crafts

A protocol for the representation of traditional crafts and the tools to implement this are proposed. The proposed protocol is a method for the systematic collection and organization of digital assets and knowledge, their representation into a formal model, and their utilization for research, education, and preservation. A set of digital tools accompanies this protocol that enables the online curation of craft representations. The proposed approach was elaborated and evaluated with craft practitioners in three case studies. Lessons learned are shared and an outlook for future work is provided