# Sparse4D v3: Advancing End-to-End 3D Detection and Tracking

arXiv: https://arxiv.org/abs/2311.11722 · HTML: https://mlsanity.com/paper/2311.11722/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 53 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (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)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://mlsanity.com/paper/2203.11496/) (2022)
- [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [EVA-02: A Visual Representation for Neon Genesis](https://mlsanity.com/paper/2303.11331/) (2023)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (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)
- [Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://mlsanity.com/paper/2303.11926/) (2023)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://mlsanity.com/paper/2211.10439/) (2022)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)

## Built on by (most cited first, 30 of 70)

- [SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation](https://mlsanity.com/paper/2405.19620/) (2024)
- [MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping](https://mlsanity.com/paper/2403.15951/) (2024)
- [DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving](https://mlsanity.com/paper/2505.19381/) (2025)
- [SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving](https://mlsanity.com/paper/2404.06892/) (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)
- [Neural Radiance Field in Autonomous Driving: A Survey](https://mlsanity.com/paper/2404.13816/) (2024)
- [DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction](https://mlsanity.com/paper/2409.19972/) (2024)
- [CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow](https://mlsanity.com/paper/2403.08919/) (2024)
- [CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception](https://mlsanity.com/paper/2503.13504/) (2025)
- [DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale](https://mlsanity.com/paper/2604.00813/) (2026)
- [PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2407.17378/) (2024)
- [CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception](https://mlsanity.com/paper/2507.19239/) (2025)
- [BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving](https://mlsanity.com/paper/2401.01065/) (2024)
- [3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection](https://mlsanity.com/paper/2507.23567/) (2025)
- [SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras](https://mlsanity.com/paper/2403.10353/) (2024)
- [Make Your ViT-based Multi-view 3D Detectors Faster via Token Compression](https://mlsanity.com/paper/2409.00633/) (2024)
- [Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking](https://mlsanity.com/paper/2510.10287/) (2025)
- [VGGT-Det: Mining VGGT Internal Priors for Sensor-Geometry-Free Multi-View Indoor 3D Object Detection](https://mlsanity.com/paper/2603.00912/) (2026)
- [MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation Learning](https://mlsanity.com/paper/2403.08760/) (2024)
- [Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting](https://mlsanity.com/paper/2404.06700/) (2024)
- [RoPETR: Improving Temporal Camera-Only 3D Detection by Integrating Enhanced Rotary Position Embedding](https://mlsanity.com/paper/2504.12643/) (2025)
- [FocalAD: Local Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.11419/) (2025)
- [VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2510.23205/) (2025)
- [Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving](https://mlsanity.com/paper/2502.07631/) (2025)
- [A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects](https://mlsanity.com/paper/2506.19769/) (2025)
- [Learning High-resolution Vector Representation from Multi-Camera Images for 3D Object Detection](https://mlsanity.com/paper/2407.15354/) (2024)
- [LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation](https://mlsanity.com/paper/2411.14927/) (2024)
- [TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking](https://mlsanity.com/paper/2504.03258/) (2025)
- [SparseMeXT Unlocking the Potential of Sparse Representations for HD Map Construction](https://mlsanity.com/paper/2505.08808/) (2025)

## Abstract (arXiv)

In autonomous driving perception systems, 3D detection and tracking are the two fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D framework. We introduce two auxiliary training tasks (Temporal Instance Denoising and Quality Estimation) and propose decoupled attention to make structural improvements, leading to significant enhancements in detection performance. Additionally, we extend the detector into a tracker using a straightforward approach that assigns instance ID during inference, further highlighting the advantages of query-based algorithms. Extensive experiments conducted on the nuScenes benchmark validate the effectiveness of the proposed improvements. With ResNet50 as the backbone, we witnessed enhancements of 3.0%, 2.2%, and 7.6% in mAP, NDS, and AMOTA, achieving 46.9%, 56.1%, and 49.0%, respectively. Our best model achieved 71.9% NDS and 67.7% AMOTA on the nuScenes test set. Code will be released at urlhttps://github.com/linxuewu/Sparse4D.