# EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection

arXiv: https://arxiv.org/abs/2303.17895 · HTML: https://mlsanity.com/paper/2303.17895/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 26 of 26)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [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)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [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)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (2022)
- [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)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [FUTR3D: A Unified Sensor Fusion Framework for 3D Detection](https://mlsanity.com/paper/2203.10642/) (2022)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [DeepInteraction: 3D Object Detection via Modality Interaction](https://mlsanity.com/paper/2208.11112/) (2022)
- [Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks](https://mlsanity.com/paper/1804.02176/) (2018)
- [Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion](https://mlsanity.com/paper/2203.09780/) (2022)
- [Polar Parametrization for Vision-based Surround-View 3D Detection](https://mlsanity.com/paper/2206.10965/) (2022)
- [TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning](https://mlsanity.com/paper/2212.13979/) (2022)
- [MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones](https://mlsanity.com/paper/2207.12716/) (2022)

## Built on by (most cited first, 30 of 38)

- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [UniBEV: Multi-modal 3D Object Detection with Uniform BEV Encoders for Robustness against Missing Sensor Modalities](https://mlsanity.com/paper/2309.14516/) (2023)
- [CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2403.19104/) (2024)
- [SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2403.07284/) (2024)
- [MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detection](https://mlsanity.com/paper/2408.05945/) (2024)
- [ADMap: Anti-disturbance framework for reconstructing online vectorized HD map](https://mlsanity.com/paper/2401.13172/) (2024)
- [HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection](https://mlsanity.com/paper/2412.11489/) (2024)
- [LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera](https://mlsanity.com/paper/2502.14503/) (2025)
- [UniM2AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving](https://mlsanity.com/paper/2308.10421/) (2023)
- [Long-Tailed 3D Detection via Multi-Modal Fusion](https://mlsanity.com/paper/2312.10986/) (2023)
- [Lift-Attend-Splat: Bird's-eye-view camera-lidar fusion using transformers](https://mlsanity.com/paper/2312.14919/) (2023)
- [SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perception](https://mlsanity.com/paper/2403.10036/) (2024)
- [Weak-to-Strong 3D Object Detection with X-Ray Distillation](https://mlsanity.com/paper/2404.00679/) (2024)
- [Valeo4Cast: A Modular Approach to End-to-End Forecasting](https://mlsanity.com/paper/2406.08113/) (2024)
- [ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection](https://mlsanity.com/paper/2407.11546/) (2024)
- [EVT: Efficient View Transformation for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2411.10715/) (2024)
- [Reflective Teacher: Semi-Supervised Multimodal 3D Object Detection in Bird's-Eye-View via Uncertainty Measure](https://mlsanity.com/paper/2412.04337/) (2024)
- [Fast-BEV++: Fast by Algorithm, Deployable by Design](https://mlsanity.com/paper/2512.08237/) (2025)
- [4DRC-OCC: Robust Semantic Occupancy Prediction Through Fusion of 4D Radar and Camera](https://mlsanity.com/paper/2603.07794/) (2026)
- [Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking](https://mlsanity.com/paper/2604.25405/) (2026)
- [Towards Consistent Object Detection via LiDAR-Camera Synergy](https://mlsanity.com/paper/2405.01258/) (2024)
- [MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector](https://mlsanity.com/paper/2405.15176/) (2024)
- [PTQAT: A Hybrid Parameter-Efficient Quantization Algorithm for 3D Perception Tasks](https://mlsanity.com/paper/2508.10557/) (2025)
- [LiteFusion: Taming 3D Object Detectors from Vision-Based to Multi-Modal with Minimal Adaptation](https://mlsanity.com/paper/2512.20217/) (2025)
- [Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection](https://mlsanity.com/paper/2409.14985/) (2024)
- [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)
- [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)

In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method. However, inaccurate depth estimation remains an important constraint to the accuracy of camera-only and multi-model 3D object detection models, especially in regions where the depth changes significantly (i.e., the "depth jump" problem). In this paper, we proposed a novel Edge-aware Lift-splat-shot (EA-LSS) framework. Specifically, edge-aware depth fusion (EADF) module is proposed to alleviate the "depth jump" problem and fine-grained depth (FGD) module to further enforce refined supervision on depth. Our EA-LSS framework is compatible for any LSS-based 3D object detection models, and effectively boosts their performances with negligible increment of inference time. Experiments on nuScenes benchmarks demonstrate that EA-LSS is effective in either camera-only or multi-model models. It is worth mentioning that EA-LSS achieved the state-of-the-art performance on nuScenes test benchmarks with mAP and NDS of 76.5% and 77.6%, respectively.