In this paper, we present a new method for decoding pixel correspondences in structured light based 3D reconstruction, refer to here as Ray-Tracing codec. The key idea of Ray-Tracing codec is to correctly define the region boundaries in real number, for each layer of the Hierarchical Orthogonal Code (HOC) based on an accurate boundary estimator, and to inherit the correct region boundaries between layers sharing common boundaries. Furthermore, each region in lower layer is traced back to the upper layer for the correct correspondence between regions. This is an improvement over existing HOC decoding algorithms as the wrong decoded pixel correspondences can be greatly reduced. The experimental results have shown that the proposed Ray-Tracing codec significantly enhances the robustness and precision in depth imaging, compare with HOC and other well-known conventional approach. The proposed approach opens a greater feasibility of applying structured light based depth imaging to a 3D modeling of cluttered workspace for home service robots.

In home service robot applications, robots often deal with textured objects. The variation of surface's reflectivity may decrease the accuracy of depth measurement of structured light 3D camera. In this paper, we propose a new method to correct the boundary of structured light pattern in order to increase the accuracy of depth measurement. The algorithm first estimates the reflection index of the surface. The light stripe is deconvoluted to remove the smooth effect of the camera lens. Finally, the edge is normalised by the reflection index, and the boundary is estimated from the intersection between the corrected versions of the edge and its inverse. The experimental results show that with the proposed boundary estimator, there are significant improvements in term of surface smoothness of textured objects.

This paper presents a method of light pattern blur estimation for its application to automatic projector focus control in structured light 3D camera. To overcome shortcoming of the conventional methods for estimating blur of light pattern, we propose to normalise the light pattern image incorporating reference images. Which are made available by a structured light 3D camera. The reference black and white patterns are utilised in such a way as to eliminate the dependence of intensity variation around an edge on surface reflectivity. Then the unknown blur radius is estimated from the first derivative of normalised edge. Experimental results on both synthetic and real images verify that the proposed method is indeed independent of surface reflectivity and is accurate and sensitive enough to the small change of blur. Finally, based on the proposed light pattern blur estimation, a structured light 3D camera that is equipped with automatic multi-stage focus control in a beam projector has been built to demonstrate its capability of capturing the 3D workspace of large depth variations.

Projector-camera system has been researched for years and it has become more and more popular in 3D vision and many other applications. However, with pre-determined position of projector's lens, projector based researches were not able to deal with the scenes that have objects with different distances from the projector. In this paper, we present a projector autofocusing technique based on local blur information of the image that can overcome above limitation. The algorithm is implemented on projector-camera system, in order to focus the pattern which is projected by projector on all objects in the scene sequentially. The proposed algorithm first obtains a blur-map of the scene on the image by using a robust local blur estimator, and then the region of interest is decided by thresholding the obtained blur-map. Since the main light source is provided by projector, the proposed auto-focusing algorithm achieves a good performance with different light conditions.