Real-time scene reconstruction from depth data inevitably suffers from
occlusion, thus leading to incomplete 3D models. Partial reconstructions, in
turn, limit the performance of algorithms that leverage them for applications
in the context of, e.g., augmented reality, robotic navigation, and 3D mapping.
Most methods address this issue by predicting the missing geometry as an
offline optimization, thus being incompatible with real-time applications. We
propose a framework that ameliorates this issue by performing scene
reconstruction and semantic scene completion jointly in an incremental and
real-time manner, based on an input sequence of depth maps. Our framework
relies on a novel neural architecture designed to process occupancy maps and
leverages voxel states to accurately and efficiently fuse semantic completion
with the 3D global model. We evaluate the proposed approach quantitatively and
qualitatively, demonstrating that our method can obtain accurate 3D semantic
scene completion in real-time.

通过实时深度数据重建场景的完整三维模型存在遮挡缺失的问题。我们提出了一个基于深度图输入序列、支持实时增量和语义场景完成的框架，其中利用一种新颖的神经架构以及基于体素状态的方式来识别精确、高效的语义完成，并将其与全局三维模型相融合，从而最终达到精准高效的三维语义场景完整。