In this paper, we proposed a method for semi-rigid changed 3D point clouds registration. We first... more In this paper, we proposed a method for semi-rigid changed 3D point clouds registration. We first segment the point clouds into individual segments and then the alignment energy costs of each segment are calculated. The rough initial transformation is estimated by minimizing the energy cost using integer programming. The final registration results are obtained by rigid alignments of separated corresponded segments. Experimental result with simulated point clouds demonstrate that the concept of semi-rigid registration works well.
2013 IEEE International Conference on Image Processing, 2013
In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of th... more In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained by 3D reconstruction techniques usually differ in scale. To match scales, we estimate the ratio of scales of two given 3D point clouds. By performing PCA of spin images over different scales of two point clouds, two sets of cumulative contribution rate curves are generated. Such sets of curves can be considered to characterize the scale of the given 3D point clouds. To find the scale ratio of two point clouds, we register the two sets of curves by using a variant of ICP that estimates the ratio of scales. Simulations with the Stanford bunny and experimental results with 3D reconstructions of artificial and real scenes demonstrate that the ratio of any 3D point clouds can be effectively used for scale matching.
In this paper, we propose two methods for estimating the scales of point clouds in order to align... more In this paper, we propose two methods for estimating the scales of point clouds in order to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of the cumulative contribution rates of PCA of descriptors over different scales. Our second method directly estimates the ratio of scales (scale ratio) of two point clouds. Instead of finding the minimum, this approach registers the two sets of curves of the cumulative contribution rate of PCA by assuming that those differ only in scale. Experimental results with simulated and real scene point clouds demonstrate that the scale alignment of 3D point clouds can be effectively accomplished by our scale ratio estimation.
IEEJ Transactions on Electronics, Information and Systems, 2013
In this paper we propose a method for 3D scene change detection. We particularly target the case ... more In this paper we propose a method for 3D scene change detection. We particularly target the case when a 3D scene can be changed by disasters and accidents. Assuming that there is a 3D point cloud of the 3D scene reconstructed before the change by using a set of training images, the camera positions of newly taken query images with small changes can be estimated. To find a region of change in the query images, first, the nearest image of a query image is selected from the training images. Then, the matching between the query image and the nearest images is computed for finding a set of non-matching points as a region of change. These change regions are visualized by projecting 3D points back only to those regions. Experimental results show that our method can detect change areas correctly.
IEEJ Transactions on Electronics, Information and Systems, 2013
In this paper we propose a method for detecting 3D keypoints in a 3D point cloud for robust real-... more In this paper we propose a method for detecting 3D keypoints in a 3D point cloud for robust real-time camera tracking. Assuming that there are a number of images corresponding to the 3D point cloud, we define a 3D keypoint as a point that has corresponding 2D keypoints in many images. These 3D keypoints are expected to appear with high probability as 2D keypoints in newly taken query images. For 3D-2D matching, we embed 2D feature descriptors into the 3D keypoints. Experimental results with 3D point clouds of indoor and outdoor scenes show that the extracted 3D keypoints can be used for matching with 2D keypoints in query images.
In this paper, we present a client-server system for 3D scene change detection. A 3D scene point ... more In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change correctly.
In this paper, we proposed a method for semi-rigid changed 3D point clouds registration. We first... more In this paper, we proposed a method for semi-rigid changed 3D point clouds registration. We first segment the point clouds into individual segments and then the alignment energy costs of each segment are calculated. The rough initial transformation is estimated by minimizing the energy cost using integer programming. The final registration results are obtained by rigid alignments of separated corresponded segments. Experimental result with simulated point clouds demonstrate that the concept of semi-rigid registration works well.
2013 IEEE International Conference on Image Processing, 2013
In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of th... more In this paper we propose a method for matching the scales of 3D point clouds. 3D point sets of the same scene obtained by 3D reconstruction techniques usually differ in scale. To match scales, we estimate the ratio of scales of two given 3D point clouds. By performing PCA of spin images over different scales of two point clouds, two sets of cumulative contribution rate curves are generated. Such sets of curves can be considered to characterize the scale of the given 3D point clouds. To find the scale ratio of two point clouds, we register the two sets of curves by using a variant of ICP that estimates the ratio of scales. Simulations with the Stanford bunny and experimental results with 3D reconstructions of artificial and real scenes demonstrate that the ratio of any 3D point clouds can be effectively used for scale matching.
In this paper, we propose two methods for estimating the scales of point clouds in order to align... more In this paper, we propose two methods for estimating the scales of point clouds in order to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of the cumulative contribution rates of PCA of descriptors over different scales. Our second method directly estimates the ratio of scales (scale ratio) of two point clouds. Instead of finding the minimum, this approach registers the two sets of curves of the cumulative contribution rate of PCA by assuming that those differ only in scale. Experimental results with simulated and real scene point clouds demonstrate that the scale alignment of 3D point clouds can be effectively accomplished by our scale ratio estimation.
IEEJ Transactions on Electronics, Information and Systems, 2013
In this paper we propose a method for 3D scene change detection. We particularly target the case ... more In this paper we propose a method for 3D scene change detection. We particularly target the case when a 3D scene can be changed by disasters and accidents. Assuming that there is a 3D point cloud of the 3D scene reconstructed before the change by using a set of training images, the camera positions of newly taken query images with small changes can be estimated. To find a region of change in the query images, first, the nearest image of a query image is selected from the training images. Then, the matching between the query image and the nearest images is computed for finding a set of non-matching points as a region of change. These change regions are visualized by projecting 3D points back only to those regions. Experimental results show that our method can detect change areas correctly.
IEEJ Transactions on Electronics, Information and Systems, 2013
In this paper we propose a method for detecting 3D keypoints in a 3D point cloud for robust real-... more In this paper we propose a method for detecting 3D keypoints in a 3D point cloud for robust real-time camera tracking. Assuming that there are a number of images corresponding to the 3D point cloud, we define a 3D keypoint as a point that has corresponding 2D keypoints in many images. These 3D keypoints are expected to appear with high probability as 2D keypoints in newly taken query images. For 3D-2D matching, we embed 2D feature descriptors into the 3D keypoints. Experimental results with 3D point clouds of indoor and outdoor scenes show that the extracted 3D keypoints can be used for matching with 2D keypoints in query images.
In this paper, we present a client-server system for 3D scene change detection. A 3D scene point ... more In this paper, we present a client-server system for 3D scene change detection. A 3D scene point cloud which stored on the server is reconstructed by (structure-from-motion) SfM technique in advance. On the other hand, the client system in tablets captures query images and sent them to the server to estimate the change area. In order to find region of change, an existing change detection method has been applied into our system. Then the server sends detection result image back to mobile device and visualize it. The result of system test shows that the system could detect change correctly.
Uploads
Papers by Baowei Lin