On-the-fly Large-scale 3D Reconstruction from Multi-Camera Rigs
Yijia Guo, Tong Hu, Zhiwei Li, Liwen Hu, Keming Qian, Xitong Lin, Shengbo Chen, Tiejun Huang, Lei Ma

TL;DR
This paper introduces a real-time 3D reconstruction framework using multi-camera rigs that fuses overlapping RGB streams into a unified Gaussian model, enabling fast, robust, and high-fidelity scene reconstruction without prior calibration.
Contribution
It is the first on-the-fly 3D reconstruction method for multi-camera rigs that achieves drift-free, real-time performance with novel hierarchical initialization and optimization strategies.
Findings
Reconstructs hundreds of meters of scenes in 2 minutes.
Achieves drift-free trajectory estimation.
Maintains high reconstruction fidelity with efficiency.
Abstract
Recent advances in 3D Gaussian Splatting (3DGS) have enabled efficient free-viewpoint rendering and photorealistic scene reconstruction. While on-the-fly extensions of 3DGS have shown promise for real-time reconstruction from monocular RGB streams, they often fail to achieve complete 3D coverage due to the limited field of view (FOV). Employing a multi-camera rig fundamentally addresses this limitation. In this paper, we present the first on-the-fly 3D reconstruction framework for multi-camera rigs. Our method incrementally fuses dense RGB streams from multiple overlapping cameras into a unified Gaussian representation, achieving drift-free trajectory estimation and efficient online reconstruction. We propose a hierarchical camera initialization scheme that enables coarse inter-camera alignment without calibration, followed by a lightweight multi-camera bundle adjustment that stabilizes…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Optical Sensing Technologies · Robotics and Sensor-Based Localization
