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Traditional stereo matching approaches generally have problems in handling textureless regions, strong occlusions and reflective regions that do not satisfy a Lambertian surface assumption. In this paper, we propose to combine the predicted surface normal by deep learning to overcome these inherent difficulties in stereo matching. With the selected reliable disparities from stereo matching method...
Keyframe-based SLAM has achieved great success in terms of accuracy, efficiency and scalability. However, due to parallax requirement and delay of map expansion, traditional keyframe-based methods easily encounter the robustness problem in the challenging cases especially for fast motion with strong rotation. For AR applications in practice, these challenging cases are easily encountered, since a...
In this demo, we present RKSLAM, a robust keyframe-based monocular SLAM system that can reliably handle fast motion with strong rotation and ensure good AR experiences. We contribute two key technical contributions: a novel multi-homography based feature tracking method which is very robust and efficient, and a sliding-window based camera pose optimization scheme which imposes the motion prior constraints...
We present a novel 3D reconstruction approach using a low-cost RGB-D camera such as Microsoft Kinect. Compared with previous methods, our scanning system can work well in challenging cases where there are large repeated textures and significant depth missing problems. For robust registration, we propose to utilize both visual and geometry features and combine SFM technique to enhance the robustness...
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