Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
MCML Authors
Christoph Reich
Daniel Cremers
Prof. Dr.
Director
Abstract
Christoph Reich
Daniel Cremers
Prof. Dr.
Director
Abstract
Semantic scene completion (SSC) aims to infer both the 3D geometry and semantics of a scene from single images. In contrast to prior work on SSC that heavily relies on expensive ground-truth annotations, we approach SSC in an unsupervised setting. Our novel method, SceneDINO, adapts techniques from self-supervised representation learning and 2D unsupervised scene understanding to SSC. Our training exclusively utilizes multi-view consistency self-supervision without any form of semantic or geometric ground truth. Given a single input image, SceneDINO infers the 3D geometry and expressive 3D DINO features in a feed-forward manner. Through a novel 3D feature distillation approach, we obtain unsupervised 3D semantics. In both 3D and 2D unsupervised scene understanding, SceneDINO reaches state-of-the-art segmentation accuracy. Linear probing our 3D features matches the segmentation accuracy of a current supervised SSC approach. Additionally, we showcase the domain generalization and multi-view consistency of SceneDINO, taking the first steps towards a strong foundation for single image 3D scene understanding.
inproceedings
ICCV 2025
IEEE/CVF International Conference on Computer Vision. Honolulu, Hawai'i, Oct 19-23, 2025. To be published. Preprint available.
Authors
A. Jevtić • C. Reich • F. Wimbauer • O. Hahn • C. Rupprecht • S. Roth • D. CremersLinks
GitHubResearch Area
BibTeXKey: JRW+25