A method for point cloud data processing, comprising: obtaining first feature vectors of respective points in first point cloud data, and determining one or more first key points in the respective points in the first point cloud data according to the first feature vectors of said respective points; obtaining second key points in second point cloud data corresponding respectively to respective first key points in the one or more first key points, according to the one or more first key points and a preset first conversion parameter between the second point cloud data and the first point cloud data; wherein the first point cloud data and the second point cloud data are point cloud data of a same scenario obtained from different view angles; characterized in that the method further comprises: determining a first preset number of first neighboring points of each of the first key points according to the first point cloud data, and obtaining a fourth feature vector of each of the first key points with a trained neural network feature extraction model according to a second feature vector of each of the first neighboring points of each of the first key points, relative coordinates and laser reflection intensity of the first neighboring points; determining a second preset number of candidate key points of each of the second key points corresponding to each of the first key points according to a preset search radius and a preset grid size; determining a first preset number of second neighboring points of each of the candidate key points according to the second point cloud data, and obtaining a fifth feature vector of each of the candidate key points with the trained neural network feature extraction model according to a third feature vector of each of the second neighboring points of each of the candidate key points, relative coordinates and laser reflection intensity of the second neighboring points; and determining a matching point registered by each of the first key points, according to the fourth feature vector of each of the first key points and the fifth feature vector of each of the candidate key points. obtaining first feature vectors of respective points in first point cloud data, and determining one or more first key points in the respective points in the first point cloud data according to the first feature vectors of said respective points; obtaining second key points in second point cloud data corresponding respectively to respective first key points in the one or more first key points, according to the one or more first key points and a preset first conversion parameter between the second point cloud data and the first point cloud data; wherein the first point cloud data and the second point cloud data are point cloud data of a same scenario obtained from different view angles; characterized in that the method further comprises: determining a first preset number of first neighboring points of each of the first key points according to the first point cloud data, and obtaining a fourth feature vector of each of the first key points with a trained neural network feature extraction model according to a second feature vector of each of the first neighboring points of each of the first key points, relative coordinates and laser reflection intensity of the first neighboring points; determining a second preset number of candidate key points of each of the second key points corresponding to each of the first key points according to a preset search radius and a preset grid size; determining a first preset number of second neighboring points of each of the candidate key points according to the second point cloud data, and obtaining a fifth feature vector of each of the candidate key points with the trained neural network feature extraction model according to a third feature vector of each of the second neighboring points of each of the candidate key points, relative coordinates and laser reflection intensity of the second neighboring points; and determining a matching point registered by each of the first key points, according to the fourth feature vector of each of the first key points and the fifth feature vector of each of the candidate key points. determining a first preset number of first neighboring points of each of the first key points according to the first point cloud data, and obtaining a fourth feature vector of each of the first key points with a trained neural network feature extraction model according to a second feature vector of each of the first neighboring points of each of the first key points, relative coordinates and laser reflection intensity of the first neighboring points; determining a second preset number of candidate key points of each of the second key points corresponding to each of the first key points according to a preset search radius and a preset grid size; determining a first preset number of second neighboring points of each of the candidate key points according to the second point cloud data, and obtaining a fifth feature vector of each of the candidate key points with the trained neural network feature extraction model according to a third feature vector of each of the second neighboring points of each of the candidate key points, relative coordinates and laser reflection intensity of the second neighboring points; and determining a matching point registered by each of the first key points, according to the fourth feature vector of each of the first key points and the fifth feature vector of each of the candidate key points.