[Submitted on 13 Oct 2026]
Title:MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection
View a PDF of the paper titled MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection, by Emir Ulurak and 1 other authors
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Abstract:Monocular 3D object detection methods increasingly rely on depth-guided transformers, but they operate entirely in the perspective image plane where spatial relationships are distorted by foreshortening. We propose MonoMIP, a dual-decoder framework that adds complementary 3D geometric reasoning through Bird's-Eye View (BEV) features constructed via Multi-Height Inverse Perspective Mapping. We perform IPM at K elevation planes spanning ground to rooftop, producing an ordered stack of height-conditioned metric BEV features that reduces above-ground misprojection and exposes depth-dependent appearance variation across planes. A BEV decoder with multi-scale deformable attention aggregates this multi-height evidence per query, while a depth-guided 3D decoder provides complementary perspective-space predictions. Without extra training data, MonoMIP attains 25.84% Moderate AP3D|R40 on KITTI val and 21.66% on the official test set for Car, outperforming recent published monocular methods on both the KITTI and Waymo benchmarks.
Comments: Accepted to ACCV 2026. 18 pages main paper and 14 pages supplementary material
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.19071 [cs.CV]
(or arXiv:2610.19071v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.19071
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Emir Ulurak [view email] [v1] Tue, 13 Oct 2026 19:06:51 UTC (22,745 KB)
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