3D Reconstruction from single RGB-D Images
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The research introduces a two-part architecture for 3D reconstruction from a single RGB image. The first segment utilizes a UNet that predicts a depth map from the RGB input. This depth map is then voxelized into an incomplete occupancy grid. The second segment, IF-Net, completes this incomplete data using additional supervision from the ground truth mesh. The entire pipeline is trained end-to-end using differentiable voxelization. The study demonstrates the potential of IF-Nets for reconstructing large, intricate scenes and extends its capabilities with a depth regressor and differential voxelization.