2D Semantic-Guided Semantic Scene Completion

Abstract

Semantic scene completion (SSC) aims to simultaneously perform scene completion (SC) and predict semantic categories of a 3D scene from a single depth and/or RGB image. Most existing SSC methods struggle to handle complex regions with multiple objects close to each other, especially for objects with reflective or dark surfaces. This primarily stems from two challenges: (1) the loss of geometric information due to the unreliability of depth values from sensors, and (2) the potential for semantic confusion when simultaneously predicting 3D shapes and semantic labels. To address these problems, we propose a Semantic-guided Semantic Scene Completion framework, dubbed SG-SSC, which involves Semantic-guided Fusion (SGF) and Volume-guided Semantic Predictor (VGSP). Guided by 2D semantic segmentation maps, SGF adaptively fuses RGB and depth features to compensate for the missing geometric information caused by the missing values in depth images, thus performing more robustly to unreliable depth information. VGSP exploits the mutual benefit between SC and SSC tasks, making SSC more focused on predicting the categories of voxels with high occupancy probabilities and also allowing SC to utilize semantic priors to better predict voxel occupancy. Experimental results show that SG-SSC outperforms existing state-of-the-art methods on the NYU, NYUCAD, and SemanticKITTI datasets. Models and code are available at https://github.com/aipixel/SG-SSC.