Correction: ImGeo-VoteNet: image and geometry co-supported VoteNet for RGB-D object detection

Depth cameras are becoming affordable and widely used to capture depth information in various real-world scenarios. However, depth-based object detectors rarely fully explore the geometry of objects, as well as simply concatenate textures and depths. By fully exploiting images and geometries of objects, we propose a VoteNet-based RGB-D object detection neural network (call ImGeo-VoteNet for short) to address the adverse situation of occluded and similar objects in indoor scenes. First, image votes are generated based on a set of candidate boxes from 2D detectors in RGB images to support the subsequent voting. Second, we transform the depths of any input scene to a point cloud representation for better using its geometry information. Third, we design three modules to capture multi-level contextual information at the point level, the object level and the global scene level, respectively, for alleviating data loss and distinguishing similar objects. Extensive experiments on the benchmark dataset show that ImGeo-VoteNet obtains a better accuracy of 3D object detection in complex indoor scenes than the state-of-the-art methods. For example, ImGeo-VoteNet achieves the improvement of +5.8 mAP over VoteNet. Code is available at: https://github.com/chenbaian-cs/ImGeo-VoteNet .