# SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation

arXiv: https://arxiv.org/abs/2009.12276 · HTML: https://mlsanity.com/paper/2009.12276/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View](https://mlsanity.com/paper/2306.10761/) (2023)
- [From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder](https://mlsanity.com/paper/2108.03648/) (2021)
- [MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding](https://mlsanity.com/paper/2107.00346/) (2021)
- [PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data](https://mlsanity.com/paper/2105.04169/) (2021)
- [Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud](https://mlsanity.com/paper/2108.03634/) (2021)
- [AGO: Adaptive Grounding for Open World 3D Occupancy Prediction](https://mlsanity.com/paper/2504.10117/) (2025)
- [Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization](https://mlsanity.com/paper/2108.08166/) (2021)
- [RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion](https://mlsanity.com/paper/2305.15883/) (2023)
- [A survey on deep learning approaches for data integration in autonomous driving system](https://mlsanity.com/paper/2306.11740/) (2023)
- [Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View](https://mlsanity.com/paper/2309.15465/) (2023)
- [Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR](https://mlsanity.com/paper/2209.09486/) (2022)
- [A Comparative Study of 3D Person Detection: Sensor Modalities and Robustness in Diverse Indoor and Outdoor Environments](https://mlsanity.com/paper/2602.05538/) (2026)

## Abstract (arXiv)

3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks. Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information. In this paper, we propose a generalization of PointPainting to be able to apply fusion at different levels. After the semantic augmentation of the point cloud, we encode raw point data in pillars to get geometric features and semantic point data in voxels to get semantic features and fuse them in an effective way. Experimental results on the KITTI test set show that SemanticVoxels achieves state-of-the-art performance in both 3D and bird's eye view pedestrian detection benchmarks. In particular, our approach demonstrates its strength in detecting challenging pedestrian cases and outperforms current state-of-the-art approaches.