# Super Sparse 3D Object Detection

arXiv: https://arxiv.org/abs/2301.02562 · HTML: https://mlsanity.com/paper/2301.02562/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 63 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [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)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [3DSSD: Point-based 3D Single Stage Object Detector](https://mlsanity.com/paper/2002.10187/) (2020)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://mlsanity.com/paper/2004.01658/) (2020)
- [Voxel Transformer for 3D Object Detection](https://mlsanity.com/paper/2109.02497/) (2021)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2203.08195/) (2022)
- [Embracing Single Stride 3D Object Detector with Sparse Transformer](https://mlsanity.com/paper/2112.06375/) (2021)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)

## Built on by (most cited first, 30 of 32)

- [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.11301/) (2023)
- [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://mlsanity.com/paper/2306.10013/) (2023)
- [FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin](https://mlsanity.com/paper/2311.12058/) (2023)
- [Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection](https://mlsanity.com/paper/2406.10700/) (2024)
- [Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning](https://mlsanity.com/paper/2410.08114/) (2024)
- [V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection](https://mlsanity.com/paper/2308.04409/) (2023)
- [FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels](https://mlsanity.com/paper/2308.03755/) (2023)
- [BEVHeight++: Toward Robust Visual Centric 3D Object Detection](https://mlsanity.com/paper/2309.16179/) (2023)
- [Fully Sparse Fusion for 3D Object Detection](https://mlsanity.com/paper/2304.12310/) (2023)
- [LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection](https://mlsanity.com/paper/2401.15865/) (2024)
- [Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection](https://mlsanity.com/paper/2304.12315/) (2023)
- [MV-Map: Offboard HD-Map Generation with Multi-view Consistency](https://mlsanity.com/paper/2305.08851/) (2023)
- [MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2401.16305/) (2024)
- [3D Video Object Detection with Learnable Object-Centric Global Optimization](https://mlsanity.com/paper/2303.15416/) (2023)
- [Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection](https://mlsanity.com/paper/2307.08209/) (2023)
- [Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation](https://mlsanity.com/paper/2409.11018/) (2024)
- [Redundant Queries in DETR-Based 3D Detection Methods: Unnecessary and Prunable](https://mlsanity.com/paper/2412.02054/) (2024)
- [MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection](https://mlsanity.com/paper/2401.11718/) (2024)
- [Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection](https://mlsanity.com/paper/2412.05154/) (2024)
- [Weakly Supervised 3D Object Detection with Multi-Stage Generalization](https://mlsanity.com/paper/2306.05418/) (2023)
- [VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregation](https://mlsanity.com/paper/2411.13186/) (2024)
- [Reflective Teacher: Semi-Supervised Multimodal 3D Object Detection in Bird's-Eye-View via Uncertainty Measure](https://mlsanity.com/paper/2412.04337/) (2024)
- [PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding](https://mlsanity.com/paper/2604.04933/) (2026)
- [Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2403.06166/) (2024)
- [FSMDet: Vision-guided feature diffusion for fully sparse 3D detector](https://mlsanity.com/paper/2409.06945/) (2024)
- [POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud](https://mlsanity.com/paper/2504.05649/) (2025)
- [Pillar-Voxel Fusion Network for 3D Object Detection in Airborne Hyperspectral Point Clouds](https://mlsanity.com/paper/2504.09506/) (2025)
- [MEGA-PCC: A Mamba-based Efficient Approach for Joint Geometry and Attribute Point Cloud Compression](https://mlsanity.com/paper/2512.22463/) (2025)
- [Correcting and Quantifying Systematic Errors in 3D Box Annotations for Autonomous Driving](https://mlsanity.com/paper/2601.14038/) (2026)
- [STELLAR: Scaling 3D Perception Large Models for Autonomous Driving](https://mlsanity.com/paper/2605.20390/) (2026)

## Abstract (arXiv)

As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range (200m) is much larger than Waymo Open Dataset (75m). Code is open-sourced at https://github.com/tusen-ai/SST.