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 https://github.com/NVlabs/VoxFormer.

翻译：人类能够轻松想象被遮挡物体和场景的完整三维几何结构，这种能力对于识别和理解至关重要。为使AI系统具备类似能力，我们提出VoxFormer——一种基于Transformer的语义场景补全框架，仅需2D图像即可输出完整的3D体素语义信息。该框架采用两阶段设计：首先通过深度估计生成稀疏的可见及占据体素查询，随后通过致密化阶段将稀疏体素扩展为密集3D体素。该设计的核心思想在于：2D图像中的视觉特征仅对应可见场景结构，而非被遮挡或空白区域。因此，从可见结构的特征化与预测出发更为可靠。获得稀疏查询集后，我们采用掩码自编码器设计，通过自注意力机制将信息传播至所有体素。在SemanticKITTI数据集上的实验表明，VoxFormer在几何和语义指标上分别相对提升20.0%和18.1%，同时将训练GPU显存消耗降低约45%至16GB以下。相关代码已开源至https://github.com/NVlabs/VoxFormer。