3D Object Tracking
16 papers with code • 1 benchmarks • 6 datasets
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Finally, we use a pre-rendered sparse viewpoint model to create a joint posterior probability for the object pose.
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom.
In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals.
3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval.