MehrkameraFusion

MehrkameraFusion is a term used in computer vision and imaging to denote the integration of information from multiple cameras to form a coherent representation of a scene. The objective is to enhance accuracy, robustness, resolution, and dynamic range beyond what a single camera can achieve, by leveraging complementary viewpoints and sensor data.

Core components of MehrkameraFusion include calibration, synchronization, and data alignment. Intrinsic and extrinsic camera parameters are estimated to establish geometric relationships between views, while temporal synchronization ensures that frames from different cameras correspond to the same moment in time. Fusion can then occur at various levels, such as pixel, feature, or decision levels, depending on the application and computational constraints.

Common fusion strategies range from early fusion, which combines raw or lightly processed data before higher-level processing, to late fusion, which fuses high-level information such as detected objects or depth maps. Techniques used in MehrkameraFusion include multi-view stereo for depth estimation, depth fusion across views, super-resolution from multiple images, and high dynamic range merging across cameras with different exposures. Epipolar geometry, view synthesis, and occlusion handling are typical challenges addressed in the pipeline.

Applications span several domains. In autonomous driving and robotics, MehrkameraFusion enables robust perception in complex environments. In surveillance and security, it improves coverage and accuracy. In media and virtual reality, it supports 360-degree video, panoramic imaging, and immersive experiences. The approach requires careful calibration, synchronization, and computational resources, and continues to evolve with advances in sensor technology and real-time processing.