Vision Only 3-D Shape Estimation for Autonomous Driving

Josephine Monica, Mark Campbell

Abstract: We present a probabilistic framework for detailed 3-D shape estimation and tracking using only vision measurements. Vision detections are processed via a bird's eye view representation, creating accurate detections at far ranges. A probabilistic model of the vision based point cloud measurements is learned and used in the framework. A 3-D shape model is developed by fusing a set of point cloud detections via a recursive Best Linear Unbiased Estimator (BLUE). The point cloud fusion accounts for noisy and inaccurate measurements, as well as minimizing growth of points in the 3-D shape. The use of a tracking algorithm and sensor pose enables 3-D shape estimation of dynamic objects from a moving car. Results are analyzed on experimental data, demonstrating the ability of our approach to produce more accurate and cleaner shape estimates.