Papers Material Recognition
“Material Recognition” 태그가 달린 논문 65편 · 필터 해제
Hierarchical Material Recognition from Local Appearance
We introduce a taxonomy of materials for hierarchical recognition from local appearance. Our taxonomy is motivated by vision applications and is arranged according to the physical traits of materials. We contribute a div…
Few-Shot LearningGraph AttentionMaterial RecognitionShape and Texture Recognition in Large Vision-Language Models
Shape and texture recognition is fundamental to visual perception. The ability to identify shapes regardless of orientation, texture, or context, and to recognize textures independently of their associated objects, is es…
3D Shape Recognition3D Shape RetrievalMaterial RecognitionTexture Image RetrievalSingle-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+2Temporal Binding Foundation Model for Material Property Recognition via Tactile Sequence Perception
Robots engaged in complex manipulation tasks require robust material property recognition to ensure adaptability and precision. Traditionally, visual data has been the primary source for object perception; however, it of…
Material RecognitionChebyshev Attention Depth Permutation Texture Network with Latent Texture Attribute Loss
Despite recent advances in deep texture recognition, existing methods still lack representational diversity and struggle to capture and preserve discriminative cues across stages of representation hierarchies. Moreov…
AttributeMaterial RecognitionObject-Oriented Material Classification and 3D Clustering for Improved Semantic Perception and Mapping in Mobile Robots
Classification of different object surface material types can play a significant role in the decision-making algorithms for mobile robots and autonomous vehicles. RGB-based scene-level semantic segmentation has been well…
Autonomous VehiclesClassificationClusteringDecision Making+3Integrated Sensing and Communications Framework for 6G Networks
In this paper, we propose a novel integrated sensing and communications (ISAC) framework for the sixth generation (6G) mobile networks, in which we decompose the real physical world into static environment, dynamic targe…
ISACMaterial RecognitionBeyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery
In the realm of computer vision material segmentation of natural scenes represents a challenge driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images which can be dece…
Material ClassificationMaterial RecognitionMaterial SegmentationScene Understanding+1Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
Visual recognition of materials and their states is essential for understanding the physical world, from identifying wet regions on surfaces or stains on fabrics to detecting infected areas on plants or minerals in rocks…
Material RecognitionSegmentationZero-Shot LearningZero Shot SegmentationIntelligent Parsing: An Automated Parsing Framework for Extracting Design Semantics from E-commerce Creatives
In the industrial e-commerce landscape, creative designs such as banners and posters are ubiquitous. Extracting structured semantic information from creative e-commerce design materials (manuscripts crafted by designers)…
Material RecognitionUMat: Uncertainty-Aware Single Image High Resolution Material Capture
We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images …
Active LearningImage-to-Image TranslationMaterial RecognitionSVBRDF EstimationHow Will It Drape Like? Capturing Fabric Mechanics from Depth Images
We propose a method to estimate the mechanical parameters of fabrics using a casual capture setup with a depth camera. Our approach enables to create mechanically-correct digital representations of real-world textile mat…
Data AugmentationMaterial RecognitionPerceptual DistanceTransfer LearningMateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered r…
Material RecognitionSemantic SegmentationOne-shot recognition of any material anywhere using contrastive learning with physics-based rendering
Visual recognition of materials and their states is essential for understanding most aspects of the world, from determining whether food is cooked, metal is rusted, or a chemical reaction has occurred. However, current i…
Contrastive LearningMaterial ClassificationMaterial RecognitionOne-Shot LearningA Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing
A key algorithm for understanding the world is material segmentation, which assigns a label (metal, glass, etc.) to each pixel. We find that a model trained on existing data underperforms in some settings and propose to …
Material ClassificationMaterial RecognitionMaterial SegmentationScene Parsing+1Encoding Spatial Distribution of Convolutional Features for Texture Representation
Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation. However, GAP cannot well characterize complex distributive patterns of spatial …
Material RecognitionRetrievalTexture ClassificationTexture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels
Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to …
Material RecognitionSuper-ResolutionGLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition
In this paper, we aim to recognize materials with combined use of auditory and visual perception. To this end, we construct a new dataset named GLAudio that consists of both the geometry of the object being struck an…
Material RecognitionUnsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this en…
Domain AdaptationMaterial RecognitionSuper-ResolutionUnsupervised Domain AdaptationThe joint role of geometry and illumination on material recognition
Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they ar…
Material Recognition