360 research outputs found
Accurate Eye Tracking from Dense 3D Surface Reconstructions using Single-Shot Deflectometry
Eye-tracking plays a crucial role in the development of virtual reality
devices, neuroscience research, and psychology. Despite its significance in
numerous applications, achieving an accurate, robust, and fast eye-tracking
solution remains a considerable challenge for current state-of-the-art methods.
While existing reflection-based techniques (e.g., "glint tracking") are
considered the most accurate, their performance is limited by their reliance on
sparse 3D surface data acquired solely from the cornea surface. In this paper,
we rethink the way how specular reflections can be used for eye tracking: We
propose a novel method for accurate and fast evaluation of the gaze direction
that exploits teachings from single-shot phase-measuring-deflectometry (PMD).
In contrast to state-of-the-art reflection-based methods, our method acquires
dense 3D surface information of both cornea and sclera within only one single
camera frame (single-shot). Improvements in acquired reflection surface
points("glints") of factors are easily achievable. We show the
feasibility of our approach with experimentally evaluated gaze errors of only
demonstrating a significant improvement over the current
state-of-the-art
Channel Capacity and Bounds In Mixed Gaussian-Impulsive Noise
Communication systems suffer from the mixed noise consisting of both
non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many
practical applications. However, there is little literature about the channel
capacity under mixed noise. In this paper, we prove the existence of the
capacity under p-th moment constraint and show that there are only finite mass
points in the capacity-achieving distribution. Moreover, we provide lower and
upper capacity bounds with closed forms. It is shown that the lower bounds can
degenerate to the well-known Shannon formula under special scenarios. In
addition, the capacity for specific modulations and the corresponding lower
bounds are discussed. Numerical results reveal that the capacity decreases when
the impulsiveness of the mixed noise becomes dominant and the obtained capacity
bounds are shown to be very tight
Breathing New Life into 3D Assets with Generative Repainting
Diffusion-based text-to-image models ignited immense attention from the
vision community, artists, and content creators. Broad adoption of these models
is due to significant improvement in the quality of generations and efficient
conditioning on various modalities, not just text. However, lifting the rich
generative priors of these 2D models into 3D is challenging. Recent works have
proposed various pipelines powered by the entanglement of diffusion models and
neural fields. We explore the power of pretrained 2D diffusion models and
standard 3D neural radiance fields as independent, standalone tools and
demonstrate their ability to work together in a non-learned fashion. Such
modularity has the intrinsic advantage of eased partial upgrades, which became
an important property in such a fast-paced domain. Our pipeline accepts any
legacy renderable geometry, such as textured or untextured meshes, orchestrates
the interaction between 2D generative refinement and 3D consistency enforcement
tools, and outputs a painted input geometry in several formats. We conduct a
large-scale study on a wide range of objects and categories from the
ShapeNetSem dataset and demonstrate the advantages of our approach, both
qualitatively and quantitatively. Project page:
https://www.obukhov.ai/repainting_3d_asset
Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery
The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation
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