Automatically parsing and reassembling fragmented 3D models is a critical challenge in the field of 3D modeling. However, existing methods often rely heavily on manual feature engineering, limiting their flexibility and performance. In this work, we present a novel and compact approach called ReassemblingNet that directly parses and reassembles instance-level 3D models from fragmented pieces. ReassemblingNet leverages a deep neural network architecture that efficiently analyzes cluttered 3D fragments and predicts the necessary transformation matrices to enable seamless reassembly. To support the training of this model, we curated a large-scale dataset named PvBreaks, which contains 2,800 fragmented pieces obtained by dissecting 20 pot and vase models from the Stanford Shape Benchmark. Through extensive experimentation, we demonstrate that our ReassemblingNet approach can effectively capture the intricate features present in both virtual and real-world 3D fragments, leading to successful reassembly.