Detect 3D object with monocular images using two-stage bird’s eye view and uneven grid with UniMODE
Detect 3D object with monocular images using two-stage bird’s eye view and uneven grid with UniMODE
UniMODE: Unified Monocular 3D Object Detection
arXiv paper abstract https://arxiv.org/abs/2402.18573
arXiv PDF paper https://arxiv.org/pdf/2402.18573.pdf
… monocular 3D object detection, including both indoor and outdoor scenes, holds great importance in applications
… However … various scenarios of data to train models poses challenges due to … different characteristics, e.g., diverse geometry properties and heterogeneous domain distributions.
… build a detector based on the bird’s-eye-view (BEV) detection paradigm, where the explicit feature projection is beneficial to addressing the geometry learning ambiguity when employing multiple scenarios of data to train detectors.
… split the classical BEV detection architecture into two stages and propose an uneven BEV grid design to handle the convergence instability caused by the aforementioned challenges.
… develop a sparse BEV feature projection strategy to reduce computational cost and a unified domain alignment method to handle heterogeneous domains.
… a unified detector UniMODE is derived, which surpasses the previous state-of-the-art …
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