  3D Semantic Scene Completion (SSC) can provide dense geometric and semantic
scene representations, which can be applied in the field of autonomous driving
and robotic systems. It is challenging to estimate the complete geometry and
semantics of a scene solely from visual images, and accurate depth information
is crucial for restoring 3D geometry. In this paper, we propose the first
stereo SSC method named OccDepth, which fully exploits implicit depth
information from stereo images (or RGBD images) to help the recovery of 3D
geometric structures. The Stereo Soft Feature Assignment (Stereo-SFA) module is
proposed to better fuse 3D depth-aware features by implicitly learning the
correlation between stereo images. In particular, when the input are RGBD
image, a virtual stereo images can be generated through original RGB image and
depth map. Besides, the Occupancy Aware Depth (OAD) module is used to obtain
geometry-aware 3D features by knowledge distillation using pre-trained depth
models. In addition, a reformed TartanAir benchmark, named SemanticTartanAir,
is provided in this paper for further testing our OccDepth method on SSC task.
Compared with the state-of-the-art RGB-inferred SSC method, extensive
experiments on SemanticKITTI show that our OccDepth method achieves superior
performance with improving +4.82% mIoU, of which +2.49% mIoU comes from stereo
images and +2.33% mIoU comes from our proposed depth-aware method. Our code and
trained models are available at https://github.com/megvii-research/OccDepth.
