Humans can easily imagine the complete 3D geometry of occluded objects and
scenes. This appealing ability is vital for recognition and understanding. To
enable such capability in AI systems, we propose VoxFormer, a Transformer-based
semantic scene completion framework that can output complete 3D volumetric
semantics from only 2D images. Our framework adopts a two-stage design where we
start from a sparse set of visible and occupied voxel queries from depth
estimation, followed by a densification stage that generates dense 3D voxels
from the sparse ones. A key idea of this design is that the visual features on
2D images correspond only to the visible scene structures rather than the
occluded or empty spaces. Therefore, starting with the featurization and
prediction of the visible structures is more reliable. Once we obtain the set
of sparse queries, we apply a masked autoencoder design to propagate the
information to all the voxels by self-attention. Experiments on SemanticKITTI
show that VoxFormer outperforms the state of the art with a relative
improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory
during training by ~45% to less than 16GB. Our code is available on
this https URL.

本论文提出了一种基于 Transformer 的场景语义补全框架 VoxFormer，可以从 2D 图像中输出完整的 3D 体素语义，并在测试中获得了相对 20% 的几何和 18.1% 的语义方面的提升。