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We present a near real-time method for 6-DoF tracking of an unknown objectfrom a monocular RGBD video sequence, while simultaneously performing neural 3Dreconstruction of the object. Our method works for arbitrary rigid objects,even when visual texture is largely absent. The object is assumed to besegmented in the first frame only. No additional information is required, andno assumption is made about the interaction agent. Key to our method is aNeural Object Field that is learned concurrently with a pose graph optimizationprocess in order to robustly accumulate information into a consistent 3Drepresentation capturing both geometry and appearance. A dynamic pool of posedmemory frames is automatically maintained to facilitate communication betweenthese threads. Our approach handles challenging sequences with large posechanges, partial and full occlusion, untextured surfaces, and specularhighlights. We show results on HO3D, YCBInEOAT, and BEHAVE datasets,demonstrating that our method significantly outperforms existing approaches.Project page: https://bundlesdf.github.io

BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects