# A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes

arXiv: https://arxiv.org/abs/2302.10465 · HTML: https://mlsanity.com/paper/2302.10465/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [3DSSD: Point-based 3D Single Stage Object Detector](https://mlsanity.com/paper/2002.10187/) (2020)
- [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016)
- [Deep Multi-camera People Detection](https://mlsanity.com/paper/1702.04593/) (2017)
- [3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams](https://mlsanity.com/paper/1905.00525/) (2019)
- [HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media](https://mlsanity.com/paper/2110.07235/) (2021)
- [SALSA: A Novel Dataset for Multimodal Group Behavior Analysis](https://mlsanity.com/paper/1506.06882/) (2015)
- [PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections](https://mlsanity.com/paper/1809.03605/) (2018)
- [A Low-Cost, Flexible and Portable Volumetric Capturing System](https://mlsanity.com/paper/1909.01207/) (2019)

## Built on by (most cited first, 4 of 4)

- [LiDAR-HMR: 3D Human Mesh Recovery from LiDAR](https://mlsanity.com/paper/2311.11971/) (2023)
- [Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes](https://mlsanity.com/paper/2308.00628/) (2023)
- [LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-timestamp 3D Human Pose Estimation](https://mlsanity.com/paper/2312.06409/) (2023)
- [3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review](https://mlsanity.com/paper/2509.12197/) (2025)

## Abstract (arXiv)

Multi-view imaging systems enable uniform coverage of 3D space and reduce the impact of occlusion, which is beneficial for 3D object detection and tracking accuracy. However, existing imaging systems built with multi-view cameras or depth sensors are limited by the small applicable scene and complicated composition. In this paper, we propose a wireless multi-view multi-modal 3D imaging system generally applicable to large outdoor scenes, which consists of a master node and several slave nodes. Multiple spatially distributed slave nodes equipped with cameras and LiDARs are connected to form a wireless sensor network. While providing flexibility and scalability, the system applies automatic spatio-temporal calibration techniques to obtain accurate 3D multi-view multi-modal data. This system is the first imaging system that integrates mutli-view RGB cameras and LiDARs in large outdoor scenes among existing 3D imaging systems. We perform point clouds based 3D object detection and long-term tracking using the 3D imaging dataset collected by this system. The experimental results show that multi-view point clouds greatly improve 3D object detection and tracking accuracy regardless of complex and various outdoor environments.