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$\times$ speed-up at $128^3$ resolution and maintains a similar memory footprint during inference.

翻译：我们建议 Ray-ONet 高效地从单个图像中重建详细的 3D 模型。 通过预测从相机坐标像素中反射的光线上的一系列占用概率,我们的方法 Ray-ONet 提高了与占用网络(ONet)相比的重建准确性,同时将网络的推论复杂性降低到 O( N $ $ 2美元 ) 。 因此, Ray- ONet 在ShapeNet 基准上实现了最先进的性能, 超过 20 美元 的速率, 达到 128 3 美元的分辨率, 并在推断中保持类似的记忆足迹 。