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Dense 3D reconstruction from RGB images traditionally assumes static camerapose estimates. This assumption has endured, even as recent works haveincreasingly focused on real-time methods for mobile devices. However, theassumption of a fixed pose for each image does not hold for online execution:poses from real-time SLAM are dynamic and may be updated following events suchas bundle adjustment and loop closure. This has been addressed in the RGB-Dsetting, by de-integrating past views and re-integrating them with updatedposes, but it remains largely untreated in the RGB-only setting. We formalizethis problem to define the new task of dense online reconstruction fromdynamically-posed images. To support further research, we introduce a datasetcalled LivePose containing the dynamic poses from a SLAM system running onScanNet. We select three recent reconstruction systems and apply a frameworkbased on de-integration to adapt each one to the dynamic-pose setting. Inaddition, we propose a novel, non-linear de-integration module that learns toremove stale scene content. We show that responding to pose updates is criticalfor high-quality reconstruction, and that our de-integration framework is aneffective solution.

LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses