Multi-view object detection in crowded environments presents significant challenges, particularly for occlusion management across multiple camera views. This paper introduces a novel approach that extends conventional multi-view detection to operate directly within each camera's image space. Our method finds objects bounding boxes for images from various perspectives without resorting to a bird’s eye view (BEV) representation. Thus, our approach removes the need for camera calibration by leveraging a learnable architecture that facilitates flexible transformations and improves feature fusion across perspectives to increase detection accuracy. Our model achieves Multi-Object Detection Accuracy (MODA) scores of 95.0% and 96.5% on the Wildtrack and MultiviewX datasets, respectively, significantly advancing the state of the art in multi-view detection. Furthermore, it demonstrates robust performance even without ground truth annotations, highlighting its resilience and practicality in real-world applications. These results emphasize the effectiveness of our calibration-free, multi-view object detector.