{'fraction_non_alphanumeric': 0.028913260219341975, 'fraction_numerical': 0.003988035892323031, 'mean_word_length': 5.236024844720497, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Semantic structure from motion with object and point interactions

We propose a new method for jointly detecting objects and recovering the geometry of the scene (camera pose, object and scene point 3D locations) from multiple semi-calibrated images (camera internal parameters are known). To achieve this task, our method models high level semantics (i.e. object class labels and relevant characteristics such as location and pose) and the interaction (correlations) of objects and feature points within the same view and across views. We validate our algorithm against state-of-the-art baseline methods using two public datasets - Ford Car dataset and Kinect Office dataset [1] - and show that we: i) significantly improve the camera pose estimation results compared to point-based SFM algorithm; ii) achieve better 2D and 3D object detection accuracy than using single images separately. Our algorithm is critical in many application scenarios including object manipulation and autonomous navigation.