mlsanityData to 14 Oct 2026

MonoMIP: Multi-Height Inverse Perspective Mapping for Monocular 3D Object Detection

arXiv 2610.19071 ↗First posted 13 Oct 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 45 of its 57 references that are in the map. First posted on arXiv 13 Oct 2026.

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45 of 57 references.

References · 45

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020End-to-End Object Detection with Transformerscited by 7,414
  3. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  4. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  5. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  7. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  8. 2019Objects as Pointscited by 1,175
  9. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
  10. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  11. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 713
  12. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 573
33 more
  1. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  2. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 552
  3. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 457
  4. 2021FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detectioncited by 447
  5. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 327
  6. 2019M3D-RPN: Monocular 3D Region Proposal Network for Object Detectioncited by 252
  7. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 249
  8. 2019Disentangling Monocular 3D Object Detectioncited by 217
  9. 2020SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimationcited by 204
  10. 2018Orthographic Feature Transform for Monocular 3D Object Detectioncited by 202
  11. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 191
  12. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 185
  13. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 156
  14. 2021Delving into Localization Errors for Monocular 3D Object Detectioncited by 140
  15. 2022MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformercited by 109
  16. 20183D-LaneNet: End-to-End 3D Multiple Lane Detectioncited by 107
  17. 2020DSGN: Deep Stereo Geometry Network for 3D Object Detectioncited by 101
  18. 2022Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignmentcited by 98
  19. 2022MonoDETR: Depth-guided Transformer for Monocular 3D Object Detectioncited by 86
  20. 2021Geometry-based Distance Decomposition for Monocular 3D Object Detectioncited by 84
  21. 2020A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye Viewcited by 72
  22. 2021LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detectorcited by 70
  23. 2021Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detectioncited by 67
  24. 2022Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detectioncited by 46
  25. 2024MonoCD: Monocular 3D Object Detection with Complementary Depthscited by 39
  26. 2023Learning Occupancy for Monocular 3D Object Detectioncited by 36
  27. 2022OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detectioncited by 24
  28. 2024MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priorscited by 22
  29. 2023MonoLSS: Learnable Sample Selection For Monocular 3D Detectioncited by 19
  30. 2024MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoderscited by 16
  31. 2025Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detectioncited by 10
  32. 2023Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceivercited by 8
  33. 2026Towards Intrinsic-Aware Monocular 3D Object Detectioncited by 4

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  1. 2025BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection
  2. 2026MonoPRIO: Adaptive Prior Conditioning for Unified Monocular 3D Object Detection
  3. 2022BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
  4. 2024MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection
  5. 2022BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection
  6. 2022Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection
  7. 2025AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
  8. 2026RefineAny3D: Depth Refinement as Semantic Alignment for Monocular 3D Detection