Tone-Mapped Mean-Shift Based Environment Map Sampling

3D-Kernel Foveated Rendering for Light Fields

Light fields capture both the spatial and angular rays, thus enabling free-viewpoint rendering and custom selection of the focal plane. Scientists can interactively explore pre-recorded microscopic light fields of organs, microbes, and neurons using virtual reality headsets. However, rendering high-resolution light fields at interactive frame rates requires a very high rate of texture sampling, which is challenging as the resolutions of light fields and displays continue to increase. In this article, we present an efficient algorithm to visualize 4D light fields with 3D-kernel foveated rendering (3D-KFR). The 3D-KFR scheme coupled with eye-tracking has the potential to accelerate the rendering of 4D depth-cued light fields dramatically. We have developed a perceptual model for foveated light fields by extending the KFR for the rendering of 3D meshes. On datasets of high-resolution microscopic light fields, we observe 3.47×-7.28× speedup in light field rendering with minimal perceptual loss of detail. We envision that 3D-KFR will reconcile the mutually conflicting goals of visual fidelity and rendering speed for interactive visualization of light fields.

Structure-Regularized Compressive Tracking With Online Data-Driven Sampling

Being a powerful appearance model, compressive random projection derives effective Haar-like features from non-rotated 4-D-parameterized rectangles, thus supporting fast and reliable object tracking. In this paper, we show that such successful fast compressive tracking scheme can be further significantly improved by structural regularization and online data-driven sampling. Our major contribution is threefold. First, we find that superpixel-guided compressive projection can generate more discriminative features by sufficiently capturing rich local structural information of images. Second, we propose fast directional integration that enables low-cost extraction of feasible Haar-like features from arbitrarily rotated 5-D-parameterized rectangles to realize more accurate object localization. Third, beyond naive dense uniform sampling, we present two practical online data-driven sampling strategies to produce less yet more effective candidate and training samples for object detection and classifier updating, respectively. Extensive experiments on real-world benchmark data sets validate the superior performance, i.e., much better object localization ability and robustness, of the proposed approach over state-of-the-art trackers.