Hashmod: A Hashing Method for Scalable 3D Object Detection

We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.Comment: BMVC 201

Learning Descriptors for Object Recognition and 3D Pose Estimation

Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.Comment: CVPR 201

Waltz - An exploratory visualization tool for volume data, using multiform abstract displays

Although, visualization is now widely used, misinterpretations still occur. There are three primary solutions intended to aid a user interpret data correctly. These are: displaying the data in different forms (Multiform visualization); simplifying (or abstracting) the structure of the viewed information; and linking objects and views together (allowing corresponding objects to be jointly manipulated and interrogated). These well-known visualization techniques, provide an emphasis towards the visualization display. We believe however that current visualization systems do not effectively utilise the display, for example, often placing it at the end of a long visualization process. Our visualization system, based on an adapted visualization model, allows a display method to be used throughout the visualization process, in which the user operates a 'Display (correlate) and Refine' visualization cycle. This display integration provides a useful exploration environment, where objects and Views may be directly manipulated; a set of 'portions of interest' can be selected to generate a specialized dataset. This may subsequently be further displayed, manipulated and filtered

Commodity Trade Stabilization Through International Agreements

We introduce a simple and efficient procedure for the segmentation of rigidly moving objects, imaged under an affine camera model. For this purpose we revisit the theory of "linear combination of views" (LCV), proposed by Ullman and Basri [20], which states that the set of 2d views of an object undergoing 3d rigid transformations, is embedded in a low-dimensional linear subspace that is spanned by a small number of basis views. Our work shows, that one may use this theory for motion segmentation, and cluster the trajectories of 3d objects using only two 2d basis views. We therefore propose a practical motion segmentation method, built around LCV, that is very simple to implement and use, and in addition is very fast, meaning it is well suited for real-time SfM and tracking applications. We have experimented on real image sequences, where we show good segmentation results, comparable to the state-of-the-art in literature. If we also consider computational complexity, our proposed method is one of the best performers in combined speed and accuracy. © 2011. The copyright of this document resides with its authors

On what powers cannot do

Dispositionalism is the view that the world is, ultimately, just a world of objects and their irreducible dispositions, and that such dispositions are, ultimately, the sole explanatory ground for the occurrence of events. This view is motivated, partly, by arguing that it affords, while non-necessitarian views of laws of nature do not afford, an adequate account of our intuitions about which regularities are non-accidental. I, however, argue that dispositionalism cannot adequately account for our intuitions about which regularities are non-accidental. Further, I argue that, intuitions aside, if we suppose that our world contains objects along with their irreducible dispositions, we must suppose, on pain of logical incoherence, that it contains laws of nature that are incompatible with a dispositionalist ontology. Indeed, if we sup ose a world of objects and irreducible dispositions, we will have to suppose that the most prominent views of laws of nature currently on offer are all inadequate

The Kantian Framework of Complementarity

A growing number of commentators have, in recent years, noted the important affinities in the views of Immanuel Kant and Niels Bohr. While these commentators are correct, the picture they present of the connections between Bohr and Kant is painted in broad strokes; it is open to the criticism that these affinities are merely superficial. In this essay, I provide a closer, structural, analysis of both Bohr's and Kant's views that makes these connections more explicit. In particular, I demonstrate the similarities between Bohr's argument, on the one hand, that neither the wave nor the particle description of atomic phenomena pick out an object in the ordinary sense of the word, and Kant's requirement, on the other hand, that both 'mathematical' (having to do with magnitude) and 'dynamical' (having to do with an object's interaction with other objects) principles must be applicable to appearances in order for us to determine them as objects of experience. I argue that Bohr's 'Complementarity interpretation' of quantum mechanics, which views atomic objects as idealizations, and which licenses the repeal of the principle of causality for the domain of atomic physics, is perfectly compatible with, and indeed follows naturally from a broadly Kantian epistemological framework.Comment: Slight change between this version and previous in the wording of the first paragraph of the section 'Complementarity

Alternative Archaeological Representations within Virtual Worlds

Traditional VR methods allow the user to tour and view the virtual world from different perspectives. Increasingly, more interactive and adaptive worlds are being generated, potentially allowing the user to interact with and affect objects in the virtual world. We describe and compare four models of operation that allow the publisher to generate views, with the client manipulating and affecting specific objects in the world. We demonstrate these approaches through a problem in archaeological visualization

Nominalism

‘Nominalism’ refers to a family of views about what there is. The objects we are familiar with (e.g. hands, laptops, cookies, and trees) can be characterized as concrete and particular. Nominalists agree that there are such things. But one group of nominalists denies that anything is non-particular and another group denies that anything is non-concrete. These two sorts of nominalism, referred to as ‘nominalism about universals’ and ‘nominalism about abstract objects’, have common motivations in contemporary philosophy

Russell on Introspection and Self-Knowledge

This chapter examines Bertrand Russell's developing views--roughly from 1911 to 1918--on the nature of introspective knowledge and subjects' most basic knowledge of themselves as themselves. It argues that Russell's theory of introspection distinguishes between direct awareness of individual psychological objects and features, the presentation of psychological complexes involving those objects and features, and introspective judgments which aim to correspond with them. It also explores his transition from believing that subjects enjoy introspective self-acquaintance, to believing that they only know themselves by self-description, and eventually to believing that self-knowledge is a logical construction. It concludes by sketching how Russell's views about introspection and self-knowledge change as a result of his adoption of neutral monism. Along the way, it sheds additional light on his acquaintance-based theory of knowledge, preference for logical constructions over inferred entities, and gradual progression towards neutral monism