Image-Based 3D Model Retrieval for Indoor Scenes by Simulating Scene Context

We propose a single image-based 3D model retrieval method for indoor scenes. By simulating the scene context of the input image, our method is able to handle several challenging scenarios featuring cluttered backgrounds and severe occlusions. To use our system, the user only needs to drag a few semantic bounding boxes for the query objects. The proposed approach then retrieves the most similar 3D models from the ShapeNet model repository, and aligns them with the corresponding objects automatically. This requires that the 3D models are represented by calibrated view-dependent visual elements learned from the rendered views. With the estimated occlusion relationships, the rendered model images are stacked at the corresponding locations to simulate the scene context. By conducting matching between these synthesized scenes and the input image, the most similar 3D models under the approximate poses are retrieved. Moreover, we show that the retrieving time can be significantly reduced based on a novel greedy algorithm. Experimental results demonstrate the effectiveness of our proposed method.