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 ...
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Comments