We present FlowR, a novel pipeline that bridges the gap between sparse and dense 3D reconstruction. Contrary to prior works, we learn a direct mapping between incorrect renderings and their corresponding ground-truth images, augmenting scene captures with consistent novel, generated views to improve reconstruction quality.
Given a set of *heterogeneous* input sequences of a common geographic area, we optimize a *single* dynamic scene representation that permits rendering of arbitrary viewpoints and scene configurations at interactive speeds.
We present the *first benchmark* and a *novel method* for radiance field reconstruction of dynamic urban areas from *heterogeneous, multi-sequence data*.
We present R3D3, a method for dense 3D reconstruction and ego-motion estimation in dynamic outdoor environments that iterates between geometric multi-camera optimization and monocular depth refinement.
Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions …
First method and benchmark for open-vocabulary multi-object tracking.
To track the 3D locations and trajectories of the other traffic participants at any given time, modern autonomous vehicles are equipped with multiple cameras that cover the vehicle’s full surroundings. Yet, camera-based 3D object tracking methods …
A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively …
Object tracking and 3D reconstruction are often performed together, with tracking used as input for reconstruction. However, the obtained reconstructions also provide useful information for improving tracking. We propose a novel method that closes …