In Yeop Jang

SMI 2013: Feature-aware filtering for point-set surface denoising

In this paper, we propose a simple and effective feature-aware filtering for point-set surface denoising, which can achieve a second-order surface approximation of the underlying surface. Our method consists of two stages: robust normal estimation considering sharp features and feature-preserving denoising using a local curvature based projection. The normal clustering based on neighborhood grouping allows the filter to preserve and respect several features during the denoising process. We demonstrate the strength of our method in terms of denoising, feature preservation and computational efficiency.

An iterative joint bilateral filtering for depth refinement of a 3D model

Time-of-Flight (TOF) sensor is an acquisition device that can capture the range data using an infrared pulse in real-time; however, it is unsuitable to perform fine 3D scanning due to sensing limitations such as low depth resolution and severe random noise. The depth measurements between adjacent regions are not distinct so that the reconstructed geometry often fails to express the details of an original shape. To perform noise removal without a loss of details, an edge-preserving filter by [Tomasi and Manduchi 1998]] has been adopted. The joint bilateral filter [J. Kopf 2007] is also used since it provides a better performance when additional information such as a high resolution image is available. However, the quality of acquired depth data needs more improvement to generate a more precise 3D model. In this research, an iterative algorithm is proposed based on the coarse-to-fine strategy to refine the depth information acquired from TOF sensor. Our method focuses on the reconstruction of 3D geometry from the measured depth map and color images. Our method outperforms other filtering techniques in noise reduction and the refinement of raw data.

Rendering of human skin during physical exercise

Many researchers have shown interest in the realistic rendering of a human face since it is crucial to the success of many applications in computer graphics, games and animation. However, it is difficult to represent the realistic appearance of a facial skin since it has complex physical properties and its appearance tends to vary as the situation changes. For example, the glossiness of a facial skin increases after exercise and the color of it becomes red after drink. In this paper, we introduce a method to simulate the appearance change of a human skin during physical exercise. Our key idea is that the appearance of a skin under exercise can be divided into a surface reflectance component and a subsurface scattering component so that they are approximated by a BRDF model and a BSSRDF model, respectively. The Torrance-Sparrow BRDF model describes the glossiness and the roughness that are affected by sweat and oil on the skin surface. The Multipole model [Donner and Jensen 2005] with absorption σa and scattering σs parameters represents subsurface scattering effects that occur between cells and pigments under the surface. In this paper, the parameters of each reflectance model are acquired by measurement.

Fast multiview three-dimensional reconstruction method using cost volume filtering

Abstract. As the number of customers who want to record three-dimensional (3-D) information using a mobile electronic device increases, it becomes more and more important to develop a method which quickly reconstructs a 3-D model from multiview images. A fast multiview-based 3-D reconstruction method is presented, which is suitable for the mobile environment by constructing a cost volume of the 3-D height field. This method consists of two steps: the construction of a reliable base surface and the recovery of shape details. In each step, the cost volume is constructed using photoconsistency and then it is filtered according to the multiscale. The multiscale-based cost volume filtering allows the 3-D reconstruction to maintain the overall shape and to preserve the shape details. We demonstrate the strength of the proposed method in terms of computation time, accuracy, and unconstrained acquisition environment.

A spatial-temporal method to refine a depth image

In this poster, a spatial-temporal depth image refinement is proposed. First, we perform a spatial denoising by applying Joint Bilateral Filter(JBF) to an image pyramid iteratively. Second, we employ a signed distance function(SDF) [Curless 1996] to obtain an optimal depth image from the depth video in which the temporal noise occurs between video frames.

Hybrid optical system for three-dimensional shape acquisition

Hybrid concepts are often used to improve existing methods in many fields. We developed a hybrid optical system that consists of multiple color cameras and one depth camera to make up the concavity problem of the visual hull construction. The heterogeneous data from the color cameras and the depth camera is fused in an effective way. The experimental results show that the proposed hybrid system can reconstruct concave objects successfully by combining the visual hull and the depth data.