Multimodal Colored Point Cloud to Image Alignment
This addresses the challenge of acquiring accurate real-world RGB-D data for supervised learning in geometric reconstruction, but it is incremental as it builds on existing photometric error reduction methods.
The paper tackles the problem of aligning colored point clouds with RGB images for accurate ground truth data in reconstruction, proposing a differential optimization method that minimizes photometric differences and matches color spaces, with demonstrations on synthetic and real data.
Reconstruction of geometric structures from images using supervised learning suffers from limited available amount of accurate data. One type of such data is accurate real-world RGB-D images. A major challenge in acquiring such ground truth data is the accurate alignment between RGB images and the point cloud measured by a depth scanner. To overcome this difficulty, we consider a differential optimization method that aligns a colored point cloud with a given color image through iterative geometric and color matching. In the proposed framework, the optimization minimizes the photometric difference between the colors of the point cloud and the corresponding colors of the image pixels. Unlike other methods that try to reduce this photometric error, we analyze the computation of the gradient on the image plane and propose a different direct scheme. We assume that the colors produced by the geometric scanner camera and the color camera sensor are different and therefore characterized by different chromatic acquisition properties. Under these multimodal conditions, we find the transformation between the camera image and the point cloud colors. We alternately optimize for aligning the position of the point cloud and matching the different color spaces. The alignments produced by the proposed method are demonstrated on both synthetic data with quantitative evaluation and real scenes with qualitative results.