Accurate indoor free-space segmentation is a challenging task due to the
complexity and the dynamic nature that indoor environments exhibit. We propose
an indoors free-space segmentation method that associates large depth values
with navigable regions. Our method leverages an unsupervised masking technique
that, using positive instances, generates segmentation labels based on textural
homogeneity and depth uniformity. Moreover, we generate superpixels
corresponding to areas of higher depth and align them with features extracted
from a Dense Prediction Transformer (DPT). Using the estimated free-space masks
and the DPT feature representation, a SegFormer model is fine-tuned on our
custom-collected indoor dataset. Our experiments demonstrate sufficient
performance in intricate scenarios characterized by cluttered obstacles and
challenging identification of free space.

室内自由空间分割方法，利用无监督掩蔽技术根据纹理均匀性和深度均匀性生成分割标签，结合密集预测 Transformer 中提取的特征与高深度区域生成超像素，通过在自定义室内数据集上对估计的自由空间掩码和 DPT 特征表示进行精调，展示了在复杂场景中表现良好，包括拥挤障碍物和自由空间较难识别的情况。