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Three-dimensional (3D) ultrasound is extensively used in obstetrics and gynecology, and realistic rendering results can both enhance the accuracy of diagnosis and facilitate communication between obstetricians and pregnant women. This paper proposes an interactive and realistic rendering method using global illumination effects for 3D ultrasound images with low signal-to-noise-ratio (SNR) values....
Salient object detection using RGB-D data is an emerging field in computer vision. Salient regions are often characterized by an unusual surface orientation profile with respect to the surroundings. To capture such profile, we introduce the histogram of surface orientation (HOSO) feature to measure surface orientation distribution contrast for RGB-D saliency. We propose a new unified model that integrates...
This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape information is not known, the offered algorithm estimates points on the shape boundary by using the statistics of the subsets of point cloud data. The novel approach proposed in this paper is able to find corner points in a local geometry...
In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector...
We present a minimalists but effective neural network that computes dense facial correspondences in highly unconstrained RGB images. Our network learns a per-pixel flow and a matchability mask between 2D input photographs of a person and the projection of a textured 3D face model. To train such a network, we generate a massive dataset of synthetic faces with dense labels using renderings of a morphable...
We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scene geometry, which allows for an efficient, keyframeless approach to depth estimation. The graph can...
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to help train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth, KITTI, and our own dataset demonstrate that our approach can significantly improve the...
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing videos that contain a depth map (RGBD) on a 2D screen. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired yet hard to display. Despite considerable progress in 3D display technologies, most are still expensive...
In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative...
In experimental fluid dynamics, the flow in a volume of fluid is observed by injecting high-contrast tracer particles and tracking them in multi-view video. Fluid dynamics researchers have developed variants of space-carving to reconstruct the 3D particle distribution at a given time-step, and then use relatively simple local matching to recover the motion over time. On the contrary, estimating the...
This paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear...
Signal source localization and separation are key tasks for many applications. In this paper, a new deterministic method is proposed for estimating the 3D location and separating multiple acoustic or vibration sources, simultaneously active. The method is based on TDOA measurements obtained via crosscorrelation. Then, with the information from the estimated locations, source separation is achieved...
In order to improve the estimation accuracy of radio propagation simulation using a 3D model reconstructed by depth sensors, recovering missing regions of the 3D model is important. In this paper, we report an evaluation result of indoor radio estimation accuracy by using such a completed 3D model. We first describe the evaluation scene and the measurement method of radio powers we used for experiments...
Two suitable coordinate systems overlapping at the origin were established to estimate orientation by computing the spatial relationship between them. An Inertial Measurement Unit sensor (IMU), consisting of a tri-axial gyroscope and tri-axial accelerometer was used to define reference systems. This work describes an algorithm to estimate the orientation of an object in 3D space through the optimal...
Direct method for visual odometry has gained popularity, it needs not to compute feature descriptor and uses the actual values of camera sensors directly. Hence, it is very fast. However, its accuracy and consistency are not satisfactory. Based on these considerations, we propose a tightly-coupled, optimization-based method to fuse inertial measurement unit (IMU) and visual measurement, in which uses...
This paper explores freehand physical interaction in egocentric Mixed Reality by performing a usability study on the use of hand posture estimation sensors. We report on precision, interactivity and usability metrics in a task-based user study, exploring the importance of additional visual cues when interacting. A total of 750 interactions were recorded from 30 participants performing 5 different...
This paper proposes a pseudo-dolly-in video generation method that reproduces motion parallax by applying image reconstruction processing to multi-view videos. Since dolly-in video is taken by moving a camera forward to reproduce motion parallax, we can present a sense of immersion. However, at a sporting event in a large-scale space, moving a camera is difficult. Our research generates dolly-in video...
Augmented Reality (AR) scenarios aim to provide realistic blending between real world and virtual objects. A key factor for realistic AR is thus a correct illumination simulation. This consists in estimating the characteristics of real light sources and use them to model virtual lighting. In this paper, we briefly introduce a novel method for recovering both 3D position and intensity of multiple light...
Global registration of heterogeneous ground and aerial mapping data is a challenging task. This is especially difficult in disaster response scenarios when we have no prior information on the environment and cannot assume the regular order of man-made environments or meaningful semantic cues. In this work we extensively evaluate different approaches to globally register UGV generated 3D point-cloud...
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