{"slug": "cd-rcm-generalizable-continuous-depth-novel-view-synthesis-for-reflectance", "title": "CD-RCM: Generalizable Continuous-Depth Novel View Synthesis for Reflectance Confocal Microscopy", "summary": "Researchers have developed CD-RCM, the first novel-view synthesis approach specifically for reflectance confocal microscopy (RCM), enabling continuous-depth visualization of human skin from sparse, anisotropic z-stacks. The feedforward model interpolates intermediate sections to create isotropic 3D volumes, allowing arbitrary-direction sectioning without per-patient optimization. CD-RCM achieves high-fidelity synthesis with sub-second inference, addressing RCM's unique axial imaging geometry and layer-dependent anatomical organization.", "body_md": "arXiv:2606.12635v1 Announce Type: new\nAbstract: Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution \"optical biopsies\" of human skin \\emph{in vivo} by acquiring en-face images at successive depths, forming a sparse z-stack. Due to optical limitations, these stacks are anisotropic 3D volumes with lateral resolution (0.5 $\\mu$m) $\\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning (3 $\\mu$m), limiting the interpretation of tissue. Our goal is to provide continuous-depth visualization by interpolating intermediate sections and making the 3D volume isotropic. Such a representation permits arbitrary-direction sectioning, including histopathology-like cross-sectional examination, without requiring per-patient optimization. To that end, we introduce the first RCM-specific novel-view synthesis (NVS) approach, CD-RCM, a feedforward model that predicts realistic, unseen depths from sparsely sampled RCM stacks. Classical neural rendering methods focus on reconstruction from surface-level multi-view observations. In contrast to surface-level camera views, RCM can acquire optically sectioned en-face images of tissue beyond the surface up to 200 $\\mu$m. However, during visualization of the RCM stacks, observations of the shallower sections (towards the surface) obscure the deeper ones. This unique axial imaging geometry and layer-dependent anatomical organization motivated our development of a tailored architectural and training framework that explicitly accounts for RCM's depth-resolved, occlusive imaging physics. Experiments demonstrate that CD-RCM achieves high-fidelity novel-view synthesis with sub-second inference time.", "url": "https://wpnews.pro/news/cd-rcm-generalizable-continuous-depth-novel-view-synthesis-for-reflectance", "canonical_source": "https://arxiv.org/abs/2606.12635", "published_at": "2026-06-12 04:00:00+00:00", "updated_at": "2026-06-12 04:49:34.858626+00:00", "lang": "en", "topics": ["computer-vision", "neural-networks", "generative-ai", "artificial-intelligence", "machine-learning"], "entities": ["CD-RCM", "Reflectance Confocal Microscopy", "RCM"], "alternates": {"html": "https://wpnews.pro/news/cd-rcm-generalizable-continuous-depth-novel-view-synthesis-for-reflectance", "markdown": "https://wpnews.pro/news/cd-rcm-generalizable-continuous-depth-novel-view-synthesis-for-reflectance.md", "text": "https://wpnews.pro/news/cd-rcm-generalizable-continuous-depth-novel-view-synthesis-for-reflectance.txt", "jsonld": "https://wpnews.pro/news/cd-rcm-generalizable-continuous-depth-novel-view-synthesis-for-reflectance.jsonld"}}