111, 125–131 (2019)Ĭhen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. 5, 4218–4224 (2020)Ĭaltagirone, L., Bellone, M., Svensson, L., Wahde, M.: Lidar-camera fusion for road detection using fully convolutional neural networks. 39(12), 2481–2495 (2017)Ĭai, P., Wang, S., Sun, Y., Liu, M.: Probabilistic end-to-end vehicle navigation in complex dynamic environments with multimodal sensor fusion. IEEE (2011)īadrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. In: 2011 IEEE International Conference on Robotics and Automation, pp. īadino, H., Huber, D., Park, Y., Kanade, T.: Fast and accurate computation of surface normals from range images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. KeywordsĪlvarez, J.M., Gevers, T., LeCun, Y., Lopez, A.M.: Road Scene Segmentation from a Single Image. The experimental results demonstrate that our proposed SNE module can benefit all the state-of-the-art CNNs for freespace detection, and our SNE-RoadSeg achieves the best overall performance among different datasets. For research purposes, we publish a large-scale synthetic freespace detection dataset, named Ready-to-Drive (R2D) road dataset, collected under different illumination and weather conditions. Furthermore, we propose a data-fusion CNN architecture, referred to as RoadSeg, which can extract and fuse features from both RGB images and the inferred surface normal information for accurate freespace detection. Hence, in this paper, we first introduce a novel module, named surface normal estimator (SNE), which can infer surface normal information from dense depth/disparity images with high accuracy and efficiency. Freespace can be hypothesized as a ground plane, on which the points have similar surface normals. The recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly improved semantic driving scene segmentation. Freespace detection is an essential component of visual perception for self-driving cars.
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