Ray-ONet: Efficient 3D Reconstruction From A Single RGB Image
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We propose Ray-ONet to reconstruct detailed 3D models from monocular images efficiently. By predicting a series of occupancy probabilities along a ray that is back-projected from a pixel in the camera coordinate, our method Ray-ONet improves the reconstruction accuracy in comparison with Occupancy Networks (ONet), while reducing the network inference complexity to O(N^2). As a result, Ray-ONet achieves state-of-the-art performance on the ShapeNet benchmark with more than 20× speed-up at 128^3 resolution and maintains a similar memory footprint during inference.
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