Marc Lazarovici

Medical teleconsultation was among the initial use cases for early telepresence research projects since medical treatment often requires timely intervention by highly specialized experts. When remote medical experts support interventions, a holistic view of the surgical site can increase situation awareness and improve team communication. A possible solution is the concept of immersive telepresence, where remote users virtually join the operating theater that is transmitted based on a real-time reconstruction of the local site. Enabled by the availability of RGB-D sensors and sufficient computing capability, it becomes possible to capture such a site in real time using multiple stationary sensors. The 3D reconstruction and simplification of textured surface meshes from the point clouds of a dynamic scene in real time is challenging and becomes infeasible for increasing capture volumes. This work presents a tightly integrated, stateless 3D reconstruction pipeline for dynamic, room-scale environments that generates simplified surface meshes from multiple RGB-D sensors in real time. Our algorithm operates directly on the fused, voxelized point cloud instead of populating signed-distance volumes per frame and using a marching cube variant for surface reconstruction. We extend the formulation of the dual contouring algorithm to work for point cloud data stored in an octree and interleave a vertex-clustering-based simplification before extracting the surface geometry. Our 3D reconstruction pipeline can perform a live reconstruction of six incoming depth videos at their native frame rate of 30 frames per second, enabling the reconstruction of smooth movement. Arbitrarily complex scene changes are possible since we do not store persistent information between frames. In terms of mesh quality and hole filling, our method falls between the direct mesh reconstruction and expensive global fitting of implicit functions.