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High-quality estimation of surface normal can help reduce ambiguity in manygeometry understanding problems, such as collision avoidance and occlusioninference. This paper presents a technique for estimating the normal from 3Dpoint clouds and 2D colour images. We have developed a transformer neuralnetwork that learns to utilise the hybrid information of visual semantic and 3Dgeometric data, as well as effective learning strategies. Compared to existingmethods, the information fusion of the proposed method is more effective, whichis supported by experiments. We have also built a simulation environment ofoutdoor traffic scenes in a 3D rendering engine to obtain annotated data totrain the normal estimator. The model trained on synthetic data is tested onthe real scenes in the KITTI dataset. And subsequent tasks built upon theestimated normal directions in the KITTI dataset show that the proposedestimator has advantage over existing methods.

Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics