Piece-wise planar 3D reconstruction simultaneously segments plane instances
and recovers their 3D plane parameters from an image, which is particularly
useful for indoor or man-made environments. Efficient reconstruction of 3D
planes coupled with semantic predictions offers advantages for a wide range of
applications requiring scene understanding and concurrent spatial mapping.
However, most existing planar reconstruction models either neglect semantic
predictions or do not run efficiently enough for real-time applications. We
introduce SoloPlanes, a real-time planar reconstruction model based on a
modified instance segmentation architecture which simultaneously predicts
semantics for each plane instance, along with plane parameters and piece-wise
plane instance masks. By providing multi-view guidance in feature space, we
achieve an improvement in instance mask segmentation despite only warping plane
features due to the nature of feature sharing in multi-task learning. Our model
simultaneously predicts semantics using single images at inference time, while
achieving real-time predictions at 43 FPS. The code will be released
post-publication.

基于改进的实例分割架构的实时平面重建模型 SoloPlanes，同时预测每个平面实例的语义、平面参数和分段平面实例掩码，通过提供特征空间中的多视角指导，在单张图像推理时同时预测语义，并以 43 帧每秒的实时预测效果。