| Summary: | 碩士 === 國立交通大學 === 電子研究所 === 105 === Owing to the development of the technology, the application of depth map is becoming more and more popular. To apply the depth image a high-quality depth map is necessary. Hence, how to capture a high-quality depth map has become a major research topic. Kinect for Xbox 360 made by Microsoft Company in 2010 has depth sensors to capture the depth information by the infrared light to detect the distance between the camera and the scene. It makes a breakthrough on the application of depth map. However, the depth map generated by Kinect still has many problems such as low resolution and error depth pixel so that it is hard to use in other applications. For example, in the application of 3D virtual view synthesis, the quality of depth maps directly affect the quality of synthesized image. Having a high-quality depth map is necessary. Hence, how to enhance the quality of depth map precisely has become an important issue.
In our experiments, we use Kinect for Xbox One (Kinect v2) made by Microsoft Company in 2014 to capture the color sequences and the depth sequences. We use the color sequence as the reference on the spatial domain and time domain to enhance the quality of the depth map. We obtain the depth information by the Kinect v2 and then we produce the depth map which has the same high resolution with the captured color image by software development package (SDK). Although the depth map from Kinect v2 has the enough high resolution, there are still some problems such as depth holes caused by the occlusion region due to the different view of the color camera and depth camera, the wrong depth pixels appearing in the edge of objects, the wrong depth information in the reflector, inconsistency in depth frame due to the unstable depth information. Hence, we propose an algorithm to solve these problems and improve the depth map.
We use Kinect v2 to capture the color sequences and the depth sequences at the same time. We use the color sequence as the reference in spatial domain and time domain to improve the depth map. We propose many techniques such as background construction, unreliable region detection, occlusion region detection, the depth outlier refinement based on the statistical model, the correct depth filling to enhance the quality of the depth map. At the end, we compare our refined depth map with those produced by other previous methods. The experimental results show that the quality of our depth maps is better.
|
|---|
