{'fraction_non_alphanumeric': 0.023711340206185566, 'fraction_numerical': 0.003092783505154639, 'mean_word_length': 5.264516129032258, '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}}
Object Reconstruction with Deep Learning: A Survey

Object reconstruction is one of the most crucial branches of computer vision. With the development of deep learning, many tasks have achieved remarkable improvements in computer vision. 3D reconstruction with deep learning also has attracted much attention in recent years. Deep learning methods based on CNN-based and GAN-based architectures have been adopted for 3D object prediction. In addition, researchers utilize different inputs such as RGB and depth images to achieve prediction based on different problem. In this paper, we provide a detailed overview of recent advances in 3D object reconstruction. The reviewed approaches are categorized into three groups depending on the input modality: RGB-based, depth-based and other-input-based. Particularly, we introduce the various methods and indirectly classify the shape representation. As a survey, we discuss the strong and weak points of exciting approaches.