Papers Material Segmentation
“Material Segmentation” 태그가 달린 논문 18편 · 필터 해제
UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields
Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is cruci…
Hyperspectral UnmixingMaterial SegmentationNeRFNovel View Synthesis+2GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs
Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulation, and robotic grasping. However, this a…
Material SegmentationRobotic GraspingLow-cost Robust Night-time Aerial Material Segmentation through Hyperspectral Data and Sparse Spatio-Temporal Learning
Material segmentation is a complex task, particularly when dealing with aerial data in poor lighting and atmospheric conditions. To address this, hyperspectral data from specialized cameras can be very useful in addition…
Material SegmentationSegmentationTime SeriesBeyond 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+1VQ-NeRF: Neural Reflectance Decomposition and Editing with Vector Quantization
We propose VQ-NeRF, a two-branch neural network model that incorporates Vector Quantization (VQ) to decompose and edit reflectance fields in 3D scenes. Conventional neural reflectance fields use only continuous represent…
Material SegmentationNeRFQuantizationSegmentationRobust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation
Multimodal learning seeks to utilize data from multiple sources to improve the overall performance of downstream tasks. It is desirable for redundancies in the data to make multimodal systems robust to missing or corrupt…
Material SegmentationMultimodal Sentiment AnalysisSemantic SegmentationSentiment AnalysisBenchmarking Performance of Deep Learning Model for Material Segmentation on Two HPC Systems
Performance Benchmarking of HPC systems is an ongoing effort that seeks to provide information that will allow for increased performance and improve the job schedulers that manage these systems. We develop a benchmarking…
BenchmarkingGPUMaterial SegmentationMatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction
Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in a material's appearance. Hyperspectral images, which are sets of spectral measurements sampled at mult…
Domain AdaptationMaterial SegmentationSegmentationDBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches
The objective of dense material segmentation is to identify the material categories for every image pixel. Recent studies adopt image patches to extract material features. Although the trained networks can improve the se…
Material SegmentationBooster: a Benchmark for Depth from Images of Specular and Transparent Surfaces
Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open in this field: dealing with non-Lamberti…
Depth EstimationMaterial SegmentationMonocular Depth EstimationA 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+1Open Challenges in Deep Stereo: the Booster Dataset
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transpar…
Material SegmentationMultimodal Material Segmentation
Recognition of materials from their visual appearance is essential for computer vision tasks, especially those that involve interaction with the real world. Material segmentation, i.e., dense per-pixel recognition of…
Material SegmentationSegmentationSemantic SegmentationGround material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided add…
Material ClassificationMaterial Segmentationobject-detectionObject DetectionAngular Luminance for Material Segmentation
Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of…
Material RecognitionMaterial SegmentationObject RecognitionSegmentation+1On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner
We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively …
ClusteringMaterial SegmentationMaterial Segmentation of Multi-View Satellite Imagery
Material recognition methods use image context and local cues for pixel-wise classification. In many cases only a single image is available to make a material prediction. Image sequences, routinely acquired in applicatio…
Material RecognitionMaterial SegmentationSegmentationSemantic SegmentationTowards Building an RGBD-M Scanner
We present a portable device to capture both shape and reflectance of an indoor scene. Consisting of a Kinect, an IR camera and several IR LEDs, our device allows the user to acquire data in a similar way as he/she scans…
Material Segmentation