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We present a system for automatic converting of 2D mask object predictionsand raw LiDAR point clouds into full 3D bounding boxes of objects. Because theLiDAR point clouds are partial, directly fitting bounding boxes to the pointclouds is meaningless. Instead, we suggest that obtaining good results requiressharing information between \emph{all} objects in the dataset jointly, overmultiple frames. We then make three improvements to the baseline. First, weaddress ambiguities in predicting the object rotations via direct optimizationin this space while still backpropagating rotation prediction through themodel. Second, we explicitly model outliers and task the network with learningtheir typical patterns, thus better discounting them. Third, we enforcetemporal consistency when video data is available. With these contributions,our method significantly outperforms previous work despite the fact that thosemethods use significantly more complex pipelines, 3D models and additionalhuman-annotated external sources of prior information.

Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views