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Tracking vehicles in LIDAR point clouds is a challenging task due to thesparsity of the data and the dense search space. The lack of structure in pointclouds impedes the use of convolution filters usually employed in 2D objecttracking. In addition, structuring point clouds is cumbersome and implieslosing fine-grained information. As a result, generating proposals in 3D spaceis expensive and inefficient. In this paper, we leverage the dense andstructured Bird Eye View (BEV) representation of LIDAR point clouds toefficiently search for objects of interest. We use an efficient Region ProposalNetwork and generate a small number of object proposals in 3D. Successively, werefine our selection of 3D object candidates by exploiting the similaritycapability of a 3D Siamese network. We regularize the latter 3D Siamese networkfor shape completion to enhance its discrimination capability. Our methodattempts to solve both for an efficient search space in the BEV space and ameaningful selection using 3D LIDAR point cloud. We show that the RegionProposal in the BEV outperforms Bayesian methods such as Kalman and ParticleFilters in providing proposal by a significant margin and that such candidatesare suitable for the 3D Siamese network. By training our method end-to-end, weoutperform the previous baseline in vehicle tracking by 12% / 18% in Successand Precision when using only 16 candidates.

Efficient Bird Eye View Proposals for 3D Siamese Tracking