  Sparse RGBD scene completion is a challenging task especially when
considering consistent textures and geometries throughout the entire scene.
Different from existing solutions that rely on human-designed text prompts or
predefined camera trajectories, we propose GenRC, an automated training-free
pipeline to complete a room-scale 3D mesh with high-fidelity textures. To
achieve this, we first project the sparse RGBD images to a highly incomplete 3D
mesh. Instead of iteratively generating novel views to fill in the void, we
utilized our proposed E-Diffusion to generate a view-consistent panoramic RGBD
image which ensures global geometry and appearance consistency. Furthermore, we
maintain the input-output scene stylistic consistency through textual inversion
to replace human-designed text prompts. To bridge the domain gap among
datasets, E-Diffusion leverages models trained on large-scale datasets to
generate diverse appearances. GenRC outperforms state-of-the-art methods under
most appearance and geometric metrics on ScanNet and ARKitScenes datasets, even
though GenRC is not trained on these datasets nor using predefined camera
trajectories. Project page: https://minfenli.github.io/GenRC
