# SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection

arXiv: https://arxiv.org/abs/2411.05292 · HTML: https://mlsanity.com/paper/2411.05292/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [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)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [DeepInteraction: 3D Object Detection via Modality Interaction](https://mlsanity.com/paper/2208.11112/) (2022)
- [Cross Modal Transformer: Towards Fast and Robust 3D Object Detection](https://mlsanity.com/paper/2301.01283/) (2023)
- [Weighted boxes fusion: Ensembling boxes from different object detection models](https://mlsanity.com/paper/1910.13302/) (2019)

## Built on by (most cited first, 8 of 8)

- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [Availability-aware Sensor Fusion via Unified Canonical Space](https://mlsanity.com/paper/2503.07029/) (2025)
- [EVT: Efficient View Transformation for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2411.10715/) (2024)
- [PTQAT: A Hybrid Parameter-Efficient Quantization Algorithm for 3D Perception Tasks](https://mlsanity.com/paper/2508.10557/) (2025)
- [FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection](https://mlsanity.com/paper/2603.08611/) (2026)
- [InsFusion: Rethink Instance-level LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2509.08374/) (2025)
- [SCAFusion: A Multimodal 3D Detection Framework for Small Object Detection in Lunar Surface Exploration](https://mlsanity.com/paper/2512.22503/) (2025)
- [Fusion4CA: Boosting 3D Object Detection via Comprehensive Image Exploitation](https://mlsanity.com/paper/2603.05305/) (2026)

## Abstract (arXiv)

More and more research works fuse the LiDAR and camera information to improve the 3D object detection of the autonomous driving system. Recently, a simple yet effective fusion framework has achieved an excellent detection performance, fusing the LiDAR and camera features in a unified bird's-eye-view (BEV) space. In this paper, we propose a LiDAR-camera fusion framework, named SimpleBEV, for accurate 3D object detection, which follows the BEV-based fusion framework and improves the camera and LiDAR encoders, respectively. Specifically, we perform the camera-based depth estimation using a cascade network and rectify the depth results with the depth information derived from the LiDAR points. Meanwhile, an auxiliary branch that implements the 3D object detection using only the camera-BEV features is introduced to exploit the camera information during the training phase. Besides, we improve the LiDAR feature extractor by fusing the multi-scaled sparse convolutional features. Experimental results demonstrate the effectiveness of our proposed method. Our method achieves 77.6% NDS accuracy on the nuScenes dataset, showcasing superior performance in the 3D object detection track.