3D Guided Weakly Supervised Semantic Segmentation

Weixuan Sun, Jing Zhang, Nick Barnes

Abstract: Pixel-wise clean annotation is necessary for fully-supervised semantic segmentation, which is laborious and expensive to obtain. In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with advanced sensors. We introduce a 2D-3D inference module to generate accurate pixel-wise segment proposal masks. Guided by 3D information, we first generate a point cloud of objects and calculate a per class objectness probability score for each point using projected bounding-boxes. Then we project the point cloud with objectness probabilities back to the 2D images followed by a refinement step to obtain segment proposals, which are treated as pseudo labels to train a semantic segmentation network. Our method works in a recursive manner to gradually refine the above-mentioned segment proposals. We conducted extensive experimental results on the 2D-3D-S dataset where we manually labeled a subset of images with bounding boxes. We show that the proposed method can generate accurate segment proposals when bounding box labels are available on only a small subset of training images. Performance comparison with recent state-of-the-art methods further illustrates the effectiveness of our method.