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01 Oct 2019TL;DR: This paper proposed Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network, which is able to represent high frequency texture and naturally blend with modern deep learning techniques.
Abstract: In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models.
160 citations
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TL;DR: Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network is proposed, which is able to represent high frequency texture and naturally blend with modern deep learning techniques.
Abstract: In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models.
137 citations
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TL;DR: A robust control design for automatic steering of passenger cars is described and the performance and robustness of the final controller was verified experimentally at California PATH in a series of test runs.
Abstract: This paper describes a robust control design for automatic steering of passenger cars. Previous studies showed that reliable automatic driving at highway speed may not be achieved under practical conditions with look-down reference systems which use only one sensor at the front bumper to measure the lateral displacement of the vehicle from the lane reference. An additional lateral displacement sensor is added here at the tail bumper to solve the automatic steering control problem. The control design is performed stepwise: an initial controller is determined using the parameter space approach in an invariance plane; and this controller is then refined to accommodate practical constraints and finally optimized using the multiobjective optimization program. The performance and robustness of the final controller was verified experimentally at California PATH in a series of test runs.
120 citations
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TL;DR: An internal design model called FunState (functions driven by state machines) is presented that enables the representation of different types of system components and scheduling mechanisms using a mixture of functional programming and state machines.
Abstract: In this paper, an internal design model called FunState (functions driven by state machines) is presented that enables the representation of different types of system components and scheduling mechanisms using a mixture of functional programming and state machines. It is shown how properties relevant for scheduling and verification of specification models such as Boolean dataflow, cyclostatic dataflow, synchronous dataflow, marked graphs, and communicating state machines as well as Petri nets can be represented in the FunState model of computation. Examples of methods suited for FunState are described, such as scheduling and verification. They are based on the representation of the model's state transitions in the form of a periodic graph. The feasibility of the novel approach is shown with an asynchronous transfer mode switch example.
90 citations
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03 Mar 2003
79 citations
Authors
Showing all 183 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dragan Djurdjanovic | 28 | 123 | 2794 |
Jalel Ben-Othman | 25 | 157 | 2803 |
Kenneth A. Marko | 20 | 69 | 1666 |
Stefan Kurz | 17 | 90 | 1039 |
Thilo Strauss | 9 | 16 | 373 |
Simon Burton | 8 | 21 | 290 |
Sebastian Boblest | 7 | 39 | 130 |
Nigel Tracey | 7 | 21 | 819 |
Erik Wüstner | 6 | 16 | 163 |
Ulrich Freund | 6 | 20 | 190 |
Sean C. Wyatt | 5 | 8 | 62 |
Dominique Barth | 5 | 5 | 94 |
Thomas Kruse | 5 | 14 | 57 |
Patrick Frey | 5 | 8 | 137 |
Ulrich Freund | 4 | 12 | 52 |