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Related Articles from SNS
REST3D: Reconstructing Physically Stable 3D Scenes from a Single Image
Reconstructing Physically Stable 3D Scenes from a Single Image Reconstructing physically stable 3D scenes from a single RGB image enables casual images to be converted into simulation-ready digital assets for applications such as immersive interaction and content creation. However, existing single-image reconstruction methods fall short in capturing the physical structure of a scene. As a result, they often produce geometrically plausible but physically inconsistent results, including object...
Mid-infrared single-pixel imaging at the single-photon level
arXiv:2605.30703v1 Announce Type: new Abstract: Single-pixel cameras have recently emerged as promising alternatives to multi-pixel sensors due to reduced costs and superior durability, which are particularly attractive for mid-infrared (MIR) imaging pertinent to applications including industry inspection and biomedical diagnosis. To date, MIR single-pixel photon-sparse imaging has yet been realized, which urgently calls for high-sensitivity optical detectors and high-fidelity spatial...
Efficient and Training-Free Single-Image Diffusion Models
Computer Science > Computer Vision and Pattern Recognition [Submitted on 3 Jun 2026] Title:Efficient and Training-Free Single-Image Diffusion Models View PDF HTML (experimental)Abstract:We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image.
ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring
Electrical Engineering and Systems Science > Image and Video Processing [Submitted on 4 Jun 2026] Title:ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurring View PDF HTML (experimental)Abstract:We introduce ErA (Error-Aware Deep Unrolling Network), an end-to-end frame work for single-image defocus deblurring.
Efficient and Training-Free Single-Image Diffusion Models
arXiv:2606.04299v1 Announce Type: new Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image. But even in this setting, training is computationally expensive and requires hours of optimization.
SimuScene: Simulation-Ready Compositional 3D Scene Reconstruction from a Single Image
arXiv:2606.03994v1 Announce Type: new Abstract: Reconstructing interactive, simulation-ready 3D scenes from a single image is a critical bottleneck for robotic manipulation. While recent single-image lifters recover plausible per-object shapes, composing them yields scenes that collapse under physical simulation due to interpenetrating, hovering, or sinking objects. Existing physics-aware methods address this strictly as a post-hoc layout correction, leaving the underlying geometric errors...
Reflection Separation from a Single Image via Joint Latent Diffusion
Announce Type: new Abstract: Single-image reflection separation is highly challenging under extreme conditions like glare or weak reflections. Existing methods often struggle to recover both layers in glare or weak-reflection scenarios because of insufficient information. This paper presents a diffusion model explicitly fine-tuned for this task, leveraging generative diffusion priors for robust separation.
MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because standard discriminators evaluate overall image naturalness rather than strict conditional realism. To address this, we propose MaCo-GAN, a novel manifold-contrastive GAN framework that replaces the conventional adversarial loss with a supervised contrastive objective. A core component of our method is a dynamic fake sample...
CLONE: A 3DGS-Based Closed-Loop Differentiable Optimization Framework for Single-Image Normal Estimation
arXiv:2508.05950v2 Announce Type: replace Abstract: We propose CLONE, a 3DGS-based Closed-Loop differentiable Optimization framework for single-image Normal Estimation. The core idea is to construct an "image-geometry-image" consistency loop that unifies and jointly constrains the limitations of both paradigms: the reliance on explicit supervision without cross-domain geometric constraints in discriminative methods, and the absence of stable differentiable optimization pathways in generative...
SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration
arXiv:2606.08402v1 Announce Type: new Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence. Despite recent progress in joint layout-and-mesh generation, existing methods often rely on holistic or weakly decomposed pipelines that entangle many factors at once and demand extensive scene-level supervision, limiting their generalization to complex...