Skip to yearly menu bar Skip to main content


Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling

Leon Sick · Dominik Engel · Pedro Hermosilla · Timo Ropinski

Arch 4A-E Poster #336
[ ] [ Project Page ]
Wed 19 Jun 10:30 a.m. PDT — noon PDT


Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this, semantic knowledge is distilled by learning to correlate randomly sampled features from images across an entire dataset. In this work, we build upon these advances by incorporating information about the structure of the scene into the training process through the use of depth information. We achieve this by (1) learning depth-feature correlation by spatially correlating the feature maps with the depth maps to induce knowledge about the structure of the scene and (2) exploiting farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene. Finally, we demonstrate the effectiveness of our technical contributions through extensive experimentation and present significant improvements in performance across multiple benchmark datasets.

Live content is unavailable. Log in and register to view live content