Better multiple 3D object track and pose estimate by do it jointly with reconstruction with 3D_MOT
Better multiple 3D object track and pose estimate by do it jointly with reconstruction with 3D_MOT
3D Multi-Object Tracking with Differentiable Pose Estimation
arXiv paper abstract https://arxiv.org/abs/2206.13785
arXiv PDF paper https://arxiv.org/pdf/2206.13785.pdf
Twitter video https://twitter.com/ak92501/status/1541954311273545728
Project page https://domischmauser.github.io/3D_MOT
... propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments.
... detect and reconstruct objects in each frame while predicting dense correspondences mappings into a normalized object space.
... leverage those correspondences to inform a graph neural network to solve for the optimal, temporally-consistent 7-DoF pose trajectories of all objects.
... novelty ... two-fold: first, ... propose a new graph-based approach for differentiable pose estimation over time to learn optimal pose trajectories;
second, ... present a joint formulation of reconstruction and pose estimation along the time axis for robust and geometrically consistent multi-object tracking.
... method improves the accumulated MOTA score ... show ... yields a significant boost in tracking performance.
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