RTLNet: Recursive Triple-Path Learning Network for Scene Parsing of RGB-D Images

Scene parsing approaches have attracted extensive attention in recent years; although several methods have been developed for scene parsing, most include complex modules for both cross-modality fusion between RGB and depth images in the encoder and image scale level recovery in the decoder under label supervision for high inference accuracy. Cross-modality information in the encoder may be diluted when processed through the decoder, and the supervision results may not be reused effectively, which adversely affects scene parsing. To address these problems, we propose a recursive triple-path learning network (RTLNet) for cross-modality interactions in the decoder using global context and cross-modality fusion modules. The proposed modules fully use cross-modality information to reduce information loss. To enhance the robustness of RTLNet, we add a path to reuse the initial predictions from the decoder and introduce a ladder-shaped feature consistency module to further leverage multiscale features. Experiments are conducted with the proposed RTLNet and nine recent RGB-D indoor scene parsing methods on the NYUv2 and SUN-RGBD indoor scene datasets; the results show that the RTLNet outperforms the other methods.