arXiv:2107.04622
공식 코드 · Papers with Code
Cumulative Assessment for Urban 3D Modeling
원문 초록
Urban 3D modeling from satellite images requires accurate semantic segmentation to delineate urban features, multiple view stereo for 3D reconstruction of surface heights, and 3D model fitting to produce compact models with accurate surface slopes. In this work, we present a cumulative assessment metric that succinctly captures error contributions from each of these components. We demonstrate our approach by providing challenging public datasets and extending two open source projects to provide an end-to-end 3D modeling baseline solution to stimulate further research and evaluation with a public leaderboard.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Mobile Image Analysis Application for Mantoux Skin Test
SpatialTrackerV2: 3D Point Tracking Made Easy
Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.