# Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection

arXiv: https://arxiv.org/abs/2311.07152 · HTML: https://mlsanity.com/paper/2311.07152/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 49 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 30 of 42)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [PointPainting: Sequential Fusion for 3D Object Detection](https://mlsanity.com/paper/1911.10150/) (2019)
- [Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://mlsanity.com/paper/1908.09492/) (2019)
- [BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection](https://mlsanity.com/paper/2203.17054/) (2022)
- [BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework](https://mlsanity.com/paper/2205.13790/) (2022)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://mlsanity.com/paper/2303.03991/) (2023)
- [Unifying Voxel-based Representation with Transformer for 3D Object Detection](https://mlsanity.com/paper/2206.00630/) (2022)
- [Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection](https://mlsanity.com/paper/2210.02443/) (2022)
- [Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://mlsanity.com/paper/2003.13402/) (2020)

## Built on by (most cited first, 28 of 28)

- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception](https://mlsanity.com/paper/2403.07746/) (2024)
- [SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2403.07284/) (2024)
- [DeepInteraction++: Multi-Modality Interaction for Autonomous Driving](https://mlsanity.com/paper/2408.05075/) (2024)
- [DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2409.19972/) (2024)
- [Progressive Multi-Modal Fusion for Robust 3D Object Detection](https://mlsanity.com/paper/2410.07475/) (2024)
- [FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Driving](https://mlsanity.com/paper/2408.06832/) (2024)
- [EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving](https://mlsanity.com/paper/2406.07042/) (2024)
- [SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2411.19860/) (2024)
- [Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras](https://mlsanity.com/paper/2502.19630/) (2025)
- [Panoptic Perception for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.15388/) (2024)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection](https://mlsanity.com/paper/2507.04369/) (2025)
- [UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs](https://mlsanity.com/paper/2511.01768/) (2025)
- [All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles](https://mlsanity.com/paper/2510.26641/) (2025)
- [BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV Alignment](https://mlsanity.com/paper/2410.20969/) (2024)
- [SparseVoxFormer: Sparse Voxel-based Transformer for Multi-modal 3D Object Detection](https://mlsanity.com/paper/2503.08092/) (2025)
- [Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection](https://mlsanity.com/paper/2503.08992/) (2025)
- [An Efficient LiDAR-Camera Fusion Network for Multi-Class 3D Dynamic Object Detection and Trajectory Prediction](https://mlsanity.com/paper/2504.13647/) (2025)
- [Rethink 3D Object Detection from Physical World](https://mlsanity.com/paper/2507.00190/) (2025)
- [A Comparative Study of 3D Person Detection: Sensor Modalities and Robustness in Diverse Indoor and Outdoor Environments](https://mlsanity.com/paper/2602.05538/) (2026)
- [Control Your Queries: Heterogeneous Query Interaction for Camera-Radar Fusion](https://mlsanity.com/paper/2604.25574/) (2026)
- [Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation](https://mlsanity.com/paper/2605.16922/) (2026)
- [Improving 3D Labeling in Self-Driving by Inferring Vehicle Information using Vision Language Models](https://mlsanity.com/paper/2605.21747/) (2026)
- [Geometry-Aware Fisheye-LiDAR Fusion for Robust 3D Object Detection in Low-Overlap Setups](https://mlsanity.com/paper/2606.08844/) (2026)
- [Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics](https://mlsanity.com/paper/2606.27554/) (2026)
- [MM-BEV: Enhancing Timeliness by Computing Where and When it Matters](https://mlsanity.com/paper/2608.15437/) (2026)
- [Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving](https://mlsanity.com/paper/2608.20874/) (2026)

## Abstract (arXiv)

3D object Detection with LiDAR-camera encounters overfitting in algorithm development which is derived from the violation of some fundamental rules. We refer to the data annotation in dataset construction for theory complementing and argue that the regression task prediction should not involve the feature from the camera branch. By following the cutting-edge perspective of 'Detecting As Labeling', we propose a novel paradigm dubbed DAL. With the most classical elementary algorithms, a simple predicting pipeline is constructed by imitating the data annotation process. Then we train it in the simplest way to minimize its dependency and strengthen its portability. Though simple in construction and training, the proposed DAL paradigm not only substantially pushes the performance boundary but also provides a superior trade-off between speed and accuracy among all existing methods. With comprehensive superiority, DAL is an ideal baseline for both future work development and practical deployment. The code has been released to facilitate future work on https://github.com/HuangJunJie2017/BEVDet.