[2604.03814] InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.03814 (cs) [Submitted on 4 Apr 2026] Title:InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset Authors:Felix Stillger, Lukas Hahn, Frederik Hasecke, Tobias Meisen View a PDF of the paper titled InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset, by Felix Stillger and 3 other authors View PDF HTML (experimental) Abstract:Camera extrinsic calibration is a fundamental task in computer vision. However, precise relative pose estimation in constrained, highly distorted environments, such as in-cabin automotive monitoring (ICAM), remains challenging. We present InCaRPose, a Transformer-based architecture designed for robust relative pose prediction between image pairs, which can be used for camera extrinsic calibration. By leveraging frozen backbone features such as DINOv3 and a Transformer-based decoder, our model effectively captures the geometric relationship between a reference and a target view. Unlike traditional methods, our approach achieves absolute metric-scale translation within the physically plausible adjustment range of in-cabin camera mounts in a single inference step, which is critical for ICAM, where accurate real-world distances are required for safety-relevant perception. We specifically address the challenges of highly distorted fisheye cameras in automotive interiors by training exclusively on synthetic data. Our model is capable of generalization to rea...