uLayout: Unified Room Layout Estimation for Perspective and Panoramic Images
원문 초록
We present uLayout, a unified model for estimating room layout geometries from both perspective and panoramic images, whereas traditional solutions require different model designs for each image type. The key idea of our solution is to unify both domains into the equirectangular projection, particularly, allocating perspective images into the most suitable latitude coordinate to effectively exploit both domains seamlessly. To address the Field-of-View (FoV) difference between the input domains, we design uLayout with a shared feature extractor with an extra 1D-Convolution layer to condition each domain input differently. This conditioning allows us to efficiently formulate a column-wise feature regression problem regardless of the FoV input. This simple yet effective approach achieves competitive performance with current state-of-the-art solutions and shows for the first time a single end-to-end model for both domains. Extensive experiments in the real-world datasets, LSUN, Matterport3D, PanoContext, and Stanford 2D-3D evidence the contribution of our approach. Code is available at https://github.com/JonathanLee112/uLayout.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
No More Ambiguity in 360° Room Layout via Bi-Layout Estimation
Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes
Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform
Semantic Room Wireframe Detection from a Single View
토론
이 논문과 연결된 포럼 글이 아직 없습니다.