Denoising diffusion models have shown a powerful capacity for generating high-quality image samples by progressively removing noise. Inspired by this, we present a diffusion-based mesh denoiser that progressively removes noise from mesh. In general, the iterative algorithm of diffusion models attempts to manipulate the overall structure and fine details of target meshes simultaneously. For this reason, it is difficult to apply the diffusion process to a mesh denoising task that removes artifacts while maintaining a structure. To address this, we formulate a structure-preserving diffusion process. Instead of diffusing the mesh vertices to be distributed as zero-centered isotopic Gaussian distribution, we diffuse each vertex into a specific noise distribution, in which the entire structure can be preserved. In addition, we propose a topology-agnostic mesh diffusion model by projecting the vertex into multiple 2-D viewpoints to efficiently learn the diffusion using a deep network. This enables the proposed method to learn the diffusion of arbitrary meshes that have an irregular topology. Finally, the denoised mesh can be obtained via refinement based on 2-D projections obtained from reverse diffusion. Through extensive experiments, we demonstrate that our method outperforms the state-of-the-art mesh denoising methods in both quantitative and qualitative evaluations.

Although various stereo matching methods are studied in many years, the accurate 3D reconstruction from multiview stereos in high-fidelity is still challenging due to the surface inconsistency caused by various factors such as specular illumination. In this paper, we propose an accurate PatchMatch based multiview stereo matching method with a quadric support window that efficiently captures the surface of a complex structured object. Our method takes three novel contributions. Firstly, delicate surface configurations are used for representing the complex structure of an object. By using a general 3D quadric function, the structured object surfaces can be estimated more accurately. In addition, an illumination robust framework is proposed, where the patch dissimilarities are precisely measured with disentangled representation. The matching cost is defined based on disentangled measurements of the object photometric and geometric properties, balancing the pixel intensities between images robust to illumination. Lastly, a multiview propagation method is proposed to confirm shape consistency among views. Through the disparity refinement to unify plane parameters of the views, the object surface is estimated from a global perspective. Consequently, the dense and smooth 3D shape of the object is reconstructed accurately. We evaluate our proposed method on the Middlebury stereo set and conduct comprehensive experiments on facial images. Both quantitative and qualitative results demonstrate that the proposed method shows significant improvements over state-of-the-art methods.

In this paper, we present a robust framework for facial pose estimation from binocular stereoscopic vision. Unlike prior work on the facial pose estimation that employs the whole landmarks even located in the wrong position, we propose a landmark selection method to remove the erroneous landmarks for better performance, especially in the large facial pose case. For this purpose, we train a convolutional neural network (CNN) in order to measure the confidence of each facial landmark detected by using a well-known landmark detection algorithm. Also, by fitting selected landmarks to 3D space, our framework becomes more robust even when a small number of landmarks are selected. Due to the absence of public dataset for the binocular stereo facial pose, we construct facial pose data sets using a motion sensor for performance validation. In our experiments, our method achieves the higher accuracy of the pose estimation than the previous method, especially for large facial pose cases.

In this paper, we propose a novel vessel tracking method, called active vessel tracking (AVT). The proposed method retains the major advantages that most 2D segmentation methods have demonstrated for 3D tracking while overcoming the drawbacks of previous 3D vessel tracking methods. Under the assumption that the vessel is cylindrical, thereby making its cross-section elliptical, the AVT finds a plane perpendicular to the vessel axis while tracking the vessel along its length. Also, We propose a method for vessel branch detection to automatically track complete vascular networks from a single starting point, whereas the previously proposed solutions have usually been limited in handling vessel bifurcations precisely on 3D or have required considerable user interaction. Our results show that the method is robust and accurate in both synthetic and clinical cases. In an experiment on synthetic data sets, the proposed method achieved a tracking accuracy of 96.1±0.5, detecting 99.1% of the branches. In an experiment on abdominal CTA data sets, it achieved a tracking accuracy of 98.4±0.5 for six target vessels, detecting 98.3% of the branches. These results show that the proposed method can outperform previous methods for vessel tracking.