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We present a technique for simultaneous 3D reconstruction of static regionsand rigidly moving objects in a scene. An RGB-D frame is represented as acollection of features, which are points and planes. We classify the featuresinto static and dynamic regions and grow separate maps, static and object maps,for each of them. To robustly classify the features in each frame, we fusemultiple RANSAC-based registration results obtained by registering differentgroups of the features to different maps, including (1) all the features to thestatic map, (2) all the features to each object map, and (3) subsets of thefeatures, each forming a segment, to each object map. This multi-groupregistration approach is designed to overcome the following challenges: scenescan be dominated by static regions, making object tracking more difficult; andmoving object might have larger pose variation between frames compared to thestatic regions. We show qualitative results from indoor scenes with objects invarious shapes. The technique enables on-the-fly object model generation to beused for robotic manipulation.

Joint 3D Reconstruction of a Static Scene and Moving Objects