Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Separating an image into reflectance and shading layers poses a challenge for learning approaches because no large corpus of precise and realistic ground truth decompositions exists. The Intrinsic Images in the Wild~(IIW) dataset provides a sparse set of relative human reflectance judgments, which serves as a standard benchmark for intrinsic images. A number of methods use IIW to learn statistical dependencies between the images and their reflectance layer. Although learning plays an important role for high performance, we show that a standard signal processing technique achieves performance on par with current state-of-the-art. We propose a loss function for CNN learning of dense reflectance predictions. Our results show a simple pixel-wise decision, without any context or prior knowledge, is sufficient to provide a strong baseline on IIW. This sets a competitive baseline which only two other approaches surpass. We then develop a joint bilateral filtering method that implements strong prior knowledge about reflectance constancy. This filtering operation can be applied to any intrinsic image algorithm and we improve several previous results achieving a new state-of-the-art on IIW. Our findings suggest that the effect of learning-based approaches may have been over-estimated so far. Explicit prior knowledge is still at least as important to obtain high performance in intrinsic image decompositions.Comment: CVPR 201

Intrinsic Images and their Applications in Intelligent Systems

The overall goal of the thesis is to research intelligent systems and to provide one more innovative piece in the puzzle towards general artificial intelligence. Because one quickly realizes the importance of computer vision for this endeavor, and in there specifically the need to understand the 3D world through their 2D projections into images, we thoroughly investigate the field of intrinsic images in this thesis and improve the intrinsic decomposition of arbitrary images to enable smarter intelligent systems. We demonstrate the utilization of such a decomposition in the task of relighting, where the intrinsic structure is shown to improve results

Multi-target Simultaneous Exploration with Continual Connectivity

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Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots

International audienceThis paper presents a novel decentralized control strategy for a multi-robot system that enables parallel multi-target exploration while ensuring a time-varying connected topology in cluttered 3D environments. Flexible continuous connectivity is guaranteed by building upon a recent connectivity maintenance method, in which limited range, line-of-sight visibility, and collision avoidance are taken into account at the same time. Completeness of the decentralized multi-target exploration algorithm is guaranteed by dynamically assigning the robots with different motion behaviors during the exploration task. One major group is subject to a suitable downscaling of the main traveling force based on the traveling efficiency of the current leader and the direction alignment between traveling and connectivity force. This supports the leader in always reaching its current target and, on a larger time horizon, that the whole team realizes the overall task in finite time. Extensive Monte Carlo simulations with a group of several quadrotor UAVs show the scalability and effectiveness of the proposed method and experiments validate its practicability