Categorical Depth Distribution Network for Monocular 3D Object Detection
Open accessAbstract
Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene cues due to the lack of direct range measurement. Many methods attempt to directly estimate depth to assist in 3D detection, but show limited performance as a result of depth inaccuracy. Our proposed solution, Categorical Depth Distribution Network (CaDDN), uses a predicted categorical depth distribution for each pixel to project rich contextual feature information to the appropriate depth interval in 3D space. We then use the computationally efficient bird’s-eye-view projection and single-stage detector to produce the final output detections. We design CaDDN as a fully differentiable end-to-end approach for joint depth estimation and object detection. We validate our approach on the KITTI 3D object detection benchmark, where we rank 1stamong published monocular methods. We also provide the first monocular 3D detection results on the newly released Waymo Open Dataset. We provide a code release for CaDDN which is made available.
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Depth-based BEV frameworks are sensitive to the accuracy of depth values or depth distributions.
“A popular BEV framework that generates BEV features is based on depth information [46, 32, 34], but this paradigm is sensitive to the accuracy of depth values or the depth distributions.”
From BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (2022) · cites this paper as [34]
BEV-based approaches have not shown significant advantages over other paradigms in 3D object detections.
“BEV-based approaches have not shown significant advantages over other paradigm in 3D object detections [47, 31, 34].”
From BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers (2022) · cites this paper as [34]
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Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe
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Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication
Li, J., Xu, R., Liu, X., Ma, J., Chi, Z., Ma, J., & Yu, H. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3260040 · Cited by 161 · see citing works
Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review
Wang, X., Li, K., & Chehri, A. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3317372 · Cited by 168 · see citing works
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?
Harley, A. W., Fang, Z., Li, J., Ambruş, R., & Fragkiadaki, K. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160831 · Cited by 159 · see citing works
EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection
Liu, Z., Huang, T., Li, B., Chen, X., Wang, X., & Bai, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3228806 · Cited by 159 · see citing works
Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection
Liu, X., Xue, N., & Wu, T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20074 · Cited by 159 · see citing works
Vision-Centric BEV Perception: A Survey
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CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
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RCFusion: Fusing 4-D Radar and Camera With Bird’s-Eye View Features for 3-D Object Detection
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3D Object Detection From Images for Autonomous Driving: A Survey
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3D Object Detection from Images for Autonomous Driving: A Survey
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SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos
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AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection
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FB-BEV: BEV Representation from Forward-Backward View Transformations
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Deep Learning-Based Image 3-D Object Detection for Autonomous Driving: Review
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BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection
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LXL: LiDAR Excluded Lean 3D Object Detection With 4D Imaging Radar and Camera Fusion
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CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
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Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection
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Voxel Field Fusion for 3D Object Detection
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Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
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Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
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Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
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Milestones in Autonomous Driving and Intelligent Vehicles—Part II: Perception and Planning
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Collaboration Helps Camera Overtake LiDAR in 3D Detection
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GenAD: Generative End-to-End Autonomous Driving
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Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection
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Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline
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Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
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Revisiting Open World Object Detection
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Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection
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OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
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DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
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Multi-modality 3D object detection in autonomous driving: A review
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MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
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MonoCD: Monocular 3D Object Detection with Complementary Depths
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DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
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Far3D: Expanding the Horizon for Surround-View 3D Object Detection
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MonoGround: Detecting Monocular 3D Objects from the Ground
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TS-BEV: BEV object detection algorithm based on temporal-spatial feature fusion
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MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
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Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
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Bird’s-Eye-View Scene Graph for Vision-Language Navigation
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Pseudo-Mono for Monocular 3D Object Detection in Autonomous Driving
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Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
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FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection
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LATR: 3D Lane Detection from Monocular Images with Transformer
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MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion
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Monocular 3D Object Detection with Depth from Motion
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CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection
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Learning Ego 3D Representation as Ray Tracing
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Communication-Efficient Collaborative Perception via Information Filling with Codebook
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TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation
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CAPE: Camera View Position Embedding for Multi-View 3D Object Detection
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Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
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Visual Point Cloud Forecasting Enables Scalable Autonomous Driving
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Lidar Point Cloud Guided Monocular 3D Object Detection
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Uncertainty-Aware Vision-Based Metric Cross-View Geolocalization
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Chen, Z., Li, Z., Zhang, S., Fang, L., Jiang, Q., & Zhao, F. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547859 · Cited by 57 · see citing works
Gao, H., Yu, X., Xu, Y., Kim, J., & Wang, Y. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3353530 · Cited by 52 · see citing works
BEV perception for autonomous driving: State of the art and future perspectives
Zhao, J., Shi, J., & Li, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125103 · Cited by 52 · see citing works
Fully Sparse Fusion for 3D Object Detection
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SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
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MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
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Homography Loss for Monocular 3D Object Detection
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BEVHeight++: Toward Robust Visual Centric 3D Object Detection
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MonoGAE: Roadside Monocular 3D Object Detection With Ground-Aware Embeddings
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COTR: Compact Occupancy TRansformer for Vision-Based 3D Occupancy Prediction
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MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer
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MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
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Densely Constrained Depth Estimator for Monocular 3D Object Detection
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SGM3D: Stereo Guided Monocular 3D Object Detection
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Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
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- Authors
- Reading, C., Harakeh, A., Chae, J., & Waslander, S. L.
- Affiliations
- University of Toronto (CA)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 8551-8560
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3133598367
- Cited by
- 595
- References
- 82
- Citations by year
- 2026: 57 · 2025: 143 · 2024: 173 · 2023: 136 · 2022: 73 · 2021: 13
- FWCI
- 32.30
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- monocular 3D object detection, depth estimation, single-stage detectors, autonomous driving, Waymo Open Dataset, KITTI dataset
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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