We present the first study of cross-sensor view synthesis across different modalities. We examine a practical, fundamental, yet widely overlooked problem: getting aligned RGB-X data, where most RGB-X prior work assumes such pairs exist and focuses on modality fusion, but it empirically requires huge engineering effort in calibration. We propose a match-densify-consolidate method. First, we perform RGB-X image matching followed by guided point densification. Using the proposed confidence-aware densification and self-matching filtering, we attain better view synthesis and later consolidate them in 3D Gaussian Splatting (3DGS). Our method uses no 3D priors for X-sensor and only assumes nearly no-cost COLMAP for RGB. We aim to remove the cumbersome calibration for various RGB-X sensors and advance the popularity of cross-sensor learning by a scalable solution that breaks through the bottleneck in large-scale real-world RGB-X data collection.
@article{arxiv.2602.23559,
title = {No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency},
author = {Cho-Ying Wu and Zixun Huang and Xinyu Huang and Liu Ren},
journal= {arXiv preprint arXiv:2602.23559},
year = {2026}
}
Comments
CVPR 2026 Main Conference. Project page: https://choyingw.github.io/3d-rgbx.github.io/