Depth recovery in robot vision is an essential problem to infer the three dimensional geometry of scenes from a sequence of the two dimensional images. In the past, many studies have been proposed for the depth estimation such as stereopsis, motion parallax and blurring phenomena. Among cues for depth estimation, depth from lens translation is based on shape from motion by using feature points....

In this paper a new coding method for multiview depth video is presented. Considering the smooth structure and sharp edges of depth maps, a segmentation based approach is proposed. This allows further preserving the depth contours thus introducing fewer artifacts in the depth perception of the video. To reduce the cost associated with partition coding, an estimation of the depth partition is bu...

Depth extraction with a mobile stereo system is described. The stereo setup is precalibrated, but the system extracts its own motion. Emphasis lies on the integration of the motion and stereo cues. It is guided by the relative confidence that the system has in these cues. This weighing is fine-grained in that it is determined for every pixel at every iteration. Reliable information spreads fast...

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera’s aperture as supervision. Prior works use a depth sensor’s outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To en...

As demand for advanced photographic applications on hand-held devices grows, these electronics require the capture of high quality depth. However, under low-light conditions, most devices still suffer from low imaging quality and inaccurate depth acquisition. To address the problem, we present a robust depth estimation method from a short burst shot with varied intensity (i.e., Auto Bracketing)...

The human visual perception heavily depends on stereoscopic vision. By fusing the two views that our eyes provide, a 3-D sensation of the surrounding is generated. It is therefore natural to assume that machine vision systems also benefit from a comparable sense. A lot of work has been done in the area of machine stereo vision, but a severe drawback of today’s algorithms is that they either ach...

This paper focuses on real-time markerless motion capture. The body pose of a person is estimated from depth images using an Iterative Closest Point algorithm. We present a very efficient approach, that estimates up to 28 degrees of freedom from 1000 data points with 4Hz. This is achieved by nonlinear optimization techniques using an analytically derived Jacobian and highly optimized correspond...

An intrinsic property of real aperture imaging has been that the observations tend to be defocused. This artifact has been used in an innovative manner by researchers for depth estimation, since the amount of defocus varies with varying depth in the scene. There have been various methods to model the defocus blur. We model the defocus process using the model of diffusion of heat. The diffusion ...

This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-In-Focus (AIF) image, which benefits each other in each stage to obtain final convergence: For the recovery of AIF image, sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation. In depth estimation step, we feed back the c...

5.1 INTRODUCTION This chapter addresses the problem of depth estimation using a sequence of images. In the previous chapter, we have eliminated the correspondence problem by taking two specific orthogonal views. In this chapter, this limitation is sought to be removed by employing point correspondence. The equation of motion derived from spherical projection will be reformulated so as to apply ...