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In this paper, we provide an early look at our model for generating terrainthat is occluded in the initial lidar scan or out of range of the sensor. As aproof of concept, we show that a transformer based framework is able to beoverfit to predict the geometries of unobserved roads around intersections orcorners. We discuss our method for generating training data, as well as aunique loss function for training our terrain extension network. The frameworkis tested on data from the SemanticKitti [1] dataset. Unlabeled point cloudsmeasured from an onboard lidar are used as input data to generate predictedroad points that are out of range or occluded in the original point-cloud scan.Then the input pointcloud and predicted terrain are concatenated to theterrain-extended pointcloud. We show promising qualitative results from thesemethods, as well as discussion for potential quantitative metrics to evaluatethe overall success of our framework. Finally, we discuss improvements that canbe made to the framework for successful generalization to test sets.

Looking Around Corners: Generative Methods in Terrain Extension