Min Sun, Chi Liu, Yu-Lun Liu, Cheng-Hao Kuo, Ming-Feng Li, Yueh-Feng Ku, Hong-Xuan Yen
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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 humandesigned 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-ofthe-art methods under most appearance and geometric metrics on Scan-Net and ARKitScenes datasets, even though GenRC is not trained on these datasets nor using predefined camera trajectories. Project
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.12939")
get_code_for_paper("2407.12939")
have("2407.12939")
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