2024. 03. 04.Multiview geometry 21 min readMultiview geometryMultiview geometry 2 Wide-Baseline Matching Patch-based model L2-Net HardNet SOSNet HyNet Dense Imaging Model GIFT GLU-Net Joint Detection and Description LIFT LF-Net SuperPoint Self-supervised for local features Loss funcVons Detector loss + Descriptor loss D2-NET Feature descriptors: 이전에 DELF 나 CNNGeometric 처럼 Dense CNN Feature를 local descriptors로 본다. 이 descripctor vector는 Euclidean distance를 계산할 준비가 된 상태이다. 실제로는 채널 방향의 L2 norm을 한다. feature detectors: raw CNN feature의 각 채널의 Post-processing을 통해 구한다 R2D2 Local Regional Information Estimation OriNet Learning to assign the local orientation values in the image matching pipeline AffNet Learning local affine shape estimator Self-supervised learning of image scale and orientation estimation : SelfScaOri Matching Models Learning to find good correspondences SuperGlue context aggregation + matching + filtering End-to-End Models LoFTR Based on transformer blocks COTR Correspondence Transformer DKM 참조