Papers Patch Matching
“Patch Matching” 태그가 달린 논문 99편 · 필터 해제
3D-GIMP: When 3D Gaussian Inpainting Meets PatchMatch
Recent advances in 3D scene editing have leveraged iterative diffusion models to update input views. However, this process is computationally expensive and struggles to produce sharp details. Meanwhile, ``hallucination d…
3D Reconstruction3D scene EditingPatch MatchingNIFTY: a Non-Local Image Flow Matching for Texture Synthesis
This paper addresses the problem of exemplar-based texture synthesis. We introduce NIFTY, a hybrid framework that combines recent insights on diffusion models trained with convolutional neural networks, and classical pat…
Patch MatchingReproducibility, Replicability, and Insights into Visual Document Retrieval with Late Interaction
Visual Document Retrieval (VDR) is an emerging research area that focuses on encoding and retrieving document images directly, bypassing the dependence on Optical Character Recognition (OCR) for document search. A recent…
Optical Character RecognitionOptical Character Recognition (OCR)Patch MatchingRetrieval+1MicroFlow: Domain-Specific Optical Flow for Ground Deformation Estimation in Seismic Events
Dense ground displacement measurements are crucial for geological studies but are impractical to collect directly. Traditionally, displacement fields are estimated using patch matching on optical satellite images from di…
GeophysicsOptical Flow EstimationPatch MatchingThe Marine Debris Forward-Looking Sonar Datasets
Sonar sensing is fundamental for underwater robotics, but limited by capabilities of AI systems, which need large training datasets. Public data in sonar modalities is lacking. This paper presents the Marine Debris Forwa…
DiversityObjectobject-detectionObject Detection+2Fence Theorem: Preprocessing is Dual-Objective Semantic Structure Isolator in 3D Anomaly Detection
3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolat…
3D Anomaly DetectionAnomaly DetectionMathematical ProofsPatch Matching+1FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Anomaly detection methods typically require extensive normal samples from the target class for training, limiting their applicability in scenarios that require rapid adaptation, such as cold start. Zero-shot and few-shot…
Anomaly DetectionImage-text matchingPatch MatchingPosition+2SurfPatch: Enabling Patch Matching for Exploratory Stream Surface Visualization
Unlike their line-based counterparts, surface-based techniques have yet to be thoroughly investigated in flow visualization due to their significant placement, speed, perception, and evaluation challenges. This paper pre…
Patch MatchingWhy and How: Knowledge-Guided Learning for Cross-Spectral Image Patch Matching
Recently, cross-spectral image patch matching based on feature relation learning has attracted extensive attention. However, performance bottleneck problems have gradually emerged in existing methods. To address this cha…
Metric LearningPatch MatchingNeRF-Texture: Synthesizing Neural Radiance Field Textures
Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-st…
3D geometryNeRFPatch MatchingTexture SynthesisCategory-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition
Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated an impressive ability to capture virtually any visual concept in daily images. However, due to the…
Patch MatchingUniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical fields. Due to the domain gaps between t…
Anomaly DetectionPatch MatchingHomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation
Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements an…
Homography EstimationPatch MatchingIGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching
Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regions and large disparities remains an ope…
Patch MatchingStereo MatchingMESA: Effective Matching Redundancy Reduction by Semantic Area Segmentation
We propose MESA and DMESA as novel feature matching methods, which utilize Segment Anything Model (SAM) to effectively mitigate matching redundancy. The key insight of our methods is to establish implicit-semantic area m…
Patch MatchingEnhancing Neural Radiance Fields with Depth and Normal Completion Priors from Sparse Views
Neural Radiance Fields (NeRF) are an advanced technology that creates highly realistic images by learning about scenes through a neural network model. However, NeRF often encounters issues when there are not enough image…
NeRFPatch MatchingHySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting
Inpainting, for filling missing image regions, is a crucial task in various applications, such as medical imaging and remote sensing. Trending data-driven approaches efficiency, for image inpainting, often requires exten…
Image InpaintingPatch MatchingTime SeriesTime Series ForecastingRelational Representation Learning Network for Cross-Spectral Image Patch Matching
Recently, feature relation learning has drawn widespread attention in cross-spectral image patch matching. However, existing related research focuses on extracting diverse relations between image patch features and ignor…
Patch MatchingRepresentation LearningGaussianPro: 3D Gaussian Splatting with Progressive Propagation
The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initializ…
3DGSNeural RenderingPatch Matching3D Feature Tracking via Event Camera
This paper presents the first 3D feature tracking method with the corresponding dataset. Our proposed method takes event streams from stereo event cameras as input to predict 3D trajectories of the target features wi…
Motion CompensationPatch MatchingPosition