Papers Intrinsic Image Decomposition
“Intrinsic Image Decomposition” 태그가 달린 논문 85편 · 필터 해제
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised Training
Monocular depth estimation and ego-motion estimation are significant tasks for scene perception and navigation in stable, accurate and efficient robot-assisted endoscopy. To tackle lighting variations and sparse textures…
Depth EstimationIntrinsic Image DecompositionMonocular Depth EstimationMotion Estimation+1Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept
Intrinsic image decomposition (IID) is the task of separating an image into albedo and shade. In real-world scenes, it is difficult to quantitatively assess IID quality due to the unavailability of ground truth. The exis…
Intrinsic Image DecompositionSAIL: Self-supervised Albedo Estimation from Real Images with a Latent Diffusion Model
Intrinsic image decomposition aims at separating an image into its underlying albedo and shading components, isolating the base color from lighting effects to enable downstream applications such as virtual relighting and…
Intrinsic Image DecompositionPRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling
We present PRISM, a unified framework that enables multiple image generation and editing tasks in a single foundational model. Starting from a pre-trained text-to-image diffusion model, PRISM proposes an effective fine-t…
Conditional Image GenerationImage GenerationIntrinsic Image DecompositionText to Image Generation+1Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces
Self-supervised monocular depth estimation (SSMDE) has gained attention in the field of deep learning as it estimates depth without requiring ground truth depth maps. This approach typically uses a photometric consistenc…
Depth EstimationDepth PredictionIntrinsic Image DecompositionKnowledge Distillation+1Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner
Flatbed scanners have emerged as promising devices for high-resolution, single-image material capture. However, existing approaches assume very specific conditions, such as uniform diffuse illumination, which are only av…
BRDF estimationImage-to-Image TranslationIntrinsic Image DecompositionMaterial Classification+2PS-Diffusion: Photorealistic Subject-Driven Image Editing with Disentangled Control and Attention
Diffusion models pre-trained on large-scale paired image-text data achieve significant success in image editing. To convey more fine-grained visual details, subject-driven editing integrates subjects in user-provided…
Intrinsic Image DecompositionMLI-NeRF: Multi-Light Intrinsic-Aware Neural Radiance Fields
Current methods for extracting intrinsic image components, such as reflectance and shading, primarily rely on statistical priors. These methods focus mainly on simple synthetic scenes and isolated objects and struggle to…
Intrinsic Image DecompositionNeRFPseudo LabelColorful Diffuse Intrinsic Image Decomposition in the Wild
Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumin…
Color ConstancyIntrinsic Image DecompositionInverse RenderingSpecular Reflection MitigationA General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering
Modeling outdoor scenes for the synthetic 3D environment requires the recovery of reflectance/albedo information from raw images, which is an ill-posed problem due to the complicated unmodeled physics in this process (e.…
Intrinsic Image DecompositionInverse RenderingUnsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training
Unsupervised intrinsic image decomposition (IID) is the process of separating a natural image into albedo and shade without these ground truths. A recent model employing light detection and ranging (LiDAR) intensity demo…
Intrinsic Image DecompositionExploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow
We investigate a challenging task of nighttime optical flow, which suffers from weakened texture and amplified noise. These degradations weaken discriminative visual features, thus causing invalid motion feature matching…
Domain AdaptationIntrinsic Image DecompositionOptical Flow EstimationA Theory of Joint Light and Heat Transport for Lambertian Scenes
We present a novel theory that establishes the relationship between light transport in visible and thermal infrared and heat transport in solids. We show that heat generated due to light absorption can be estimated b…
Intrinsic Image DecompositionExploiting Diffusion Prior for Generalizable Dense Prediction
Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP, a pipe…
Intrinsic Image DecompositionPredictionSemantic SegmentationHyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate cl…
ClassificationHyperspectral image analysisHyperspectral Image Classificationimage-classification+2Intrinsic Image Decomposition via Ordinal Shading
Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in various inverse rendering and computational photography pipelines. Generating highly accurate intrinsic decompositions is an …
Intrinsic Image DecompositionInverse RenderingIntrinsic Image Decomposition Using Point Cloud Representation
The purpose of intrinsic decomposition is to separate an image into its albedo (reflective properties) and shading components (illumination properties). This is challenging because it's an ill-posed problem. Conventional…
Intrinsic Image DecompositionMeasured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation
Intrinsic image decomposition and inverse rendering are long-standing problems in computer vision. To evaluate albedo recovery, most algorithms report their quantitative performance with a mean Weighted Human Disagreemen…
Intrinsic Image DecompositionInverse RenderingJoIN: Joint GANs Inversion for Intrinsic Image Decomposition
In this work, we propose to solve ill-posed inverse imaging problems using a bank of Generative Adversarial Networks (GAN) as a prior and apply our method to the case of Intrinsic Image Decomposition for faces and materi…
Image RelightingIntrinsic Image DecompositionDPF: Learning Dense Prediction Fields with Weak Supervision
Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impossible (e.g., for intrinsic image decomp…
Intrinsic Image DecompositionPredictionScene ParsingScene Understanding+1