By-example Synthesis of Compactly Stored Textures

AbstractThis paper proposes a novel by-example texture synthesis algorithm. Unlike most present algorithms whose synthesized textures of different sizes must be stored in memory until rendering, our results are compactly stored as paths of the generative graphs. This is achieved by adapting the graph-based synthesis framework to be suitable for arbitrary types of textures. Two synthesis processes are carried out successively to grow the texture strip by strip to the desired horizontal and vertical dimensions, each of which is cast as a constrained shortest path problem that can be solved efficiently by our proposed algorithm. The strips are formed by consecutive cuts which are precomputed in the preprocessing step, and are selected with a redesigned mechanism accounting for both global and local matching errors. The variances make the graph-based synthesis framework also applicable to a wide variety of textures besides architectural textures, and the synthesis quality for them is greatly improved

What is Holding Back Convnets for Detection?

Convolutional neural networks have recently shown excellent results in general object detection and many other tasks. Albeit very effective, they involve many user-defined design choices. In this paper we want to better understand these choices by inspecting two key aspects "what did the network learn?", and "what can the network learn?". We exploit new annotations (Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite common belief, our results indicate that existing state-of-the-art convnet architectures are not invariant to various appearance factors. In fact, all considered networks have similar weak points which cannot be mitigated by simply increasing the training data (architectural changes are needed). We show that overall performance can improve when using image renderings for data augmentation. We report the best known results on the Pascal3D+ detection and view-point estimation tasks

archiTECTONICS: Pre- and Trans-Disciplinary Reference in Beginning Design

This presentation was part of the session : Pedagogy: Procedures, Scaffolds, Strategies, Tactics24th National Conference on the Beginning Design StudentPedagogical approaches to beginning design in architecture often assume trans-disciplinary modes of exploration to filter problem parameters and sculpt perceptual outlook for iterative potential. A closer look suggests moments within the architectural design process that come before, or around, the discipline itself in the form of other disciplines accompanied by basic principles, such as Visual Literacy. Iterating and perceiving through every disciplinary dynamic, instance, and/or action in the process of designing transcends, builds, and structures its neighbor for explorative sequencing, intention, and growth of sensibilities in design resolution. An acute awareness of disciplinary state, in a maturing design process, can alleviate obscurity of ideological foundation and facilitate growth for trans-disciplinary thinking, making, and communicating in a root discipline such as architecture. How can beginning design instructors guide young designers to keep ideas and concepts for design in focus, recognizing that root disciplines transcend pre- and trans-disciplinary processes? Does recognizing variation in pace, induced by digital and analog tools, and intention of design iteration, by discipline, instill clarity by pre-disciplinary thinking, perception, and operation? Trans-disciplinary exercise provokes awareness of pre-disciplinary foundations furthering possibilities for unique root-disciplinary understandings and results. The developed exercise, archiTECTONIC, recognizes and cycles through reasoning, conceptualization, and iteration in a trans-disciplinary sequence, allowing the beginning design student to recognize pre-disciplinary ideology, pace, and purpose when processing ideas through fundamentals of architectural design. Engaging this as a strategy for seeing, thinking, and maneuvering through a dynamic process provides design liberty and clarity for processing and communicating in a root discipline, in this case architecture