3D Object Tracking
31 papers with code • 2 benchmarks • 11 datasets
3D Object Tracking is a computer vision task dedicated to monitoring and precisely locating objects as they navigate within a three-dimensional environment. It frequently utilizes 3D object detection techniques to pinpoint the objects and establish unique identifications that persist across multiple frames.
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.
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
Digital twin is a problem of augmenting real objects with their digital counterparts.
Robust Digital-Twin Localization via An RGBD-based Transformer Network and A Comprehensive Evaluation on a Mobile Dataset
The potential of digital-twin technology, involving the creation of precise digital replicas of physical objects, to reshape AR experiences in 3D object tracking and localization scenarios is significant.
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.
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor.