The present invention discloses a real-time binocular stereo matching method based on an adaptive candidate parallax prediction network. The method comprises: first performing multi-scale feature extraction on a stereo image pair by using a two-dimensional convolutional neural network so as to obtain high-resolution and low-resolution feature maps; then, in the first-stage parallax estimation, using the low-resolution feature map to perform parallax coarse estimation; after an adaptive candidate parallax is predicted by using a coarse estimation result and left image information, in the second-stage parallax estimation, performing fine parallax estimation by using a prediction result and the high-resolution feature map; and finally, performing hierarchical refinement on a parallax map to obtain a full-size parallax map. Compared with existing coarse-to-fine stereo matching neural networks, the present invention allows for more accurate dynamic offset prediction for a fine parallax estimation stage so as to meet different parallax correction requirements of various targets in the images. Due to the effectiveness of dynamic prediction, a two-stage processing structure is designed in the present invention to greatly improve the calculation precision and speed of a real-time binocular stereo matching network.
REAL-TIME BINOCULAR STEREO MATCHING METHOD BASED ON ADAPTIVE CANDIDATE PARALLAX PREDICTION NETWORK