Papers Transparent objects
“Transparent objects” 태그가 달린 논문 117편 · 필터 해제
STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects on Production Lines
In this work, we present STOPNet, a framework for 6-DoF object suction detection on production lines, with a focus on but not limited to transparent objects, which is an important and challenging problem in robotic syste…
Transparent objectsNeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields
We present NeuralLabeling, a labeling approach and toolset for annotating 3D scenes using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF obj…
Depth CompletionInstance SegmentationNeRFObject+2TransTouch: Learning Transparent Objects Depth Sensing Through Sparse Touches
Transparent objects are common in daily life. However, depth sensing for transparent objects remains a challenging problem. While learning-based methods can leverage shape priors to improve the sensing quality, the labor…
Transparent objectsTransparent Object Tracking with Enhanced Fusion Module
Accurate tracking of transparent objects, such as glasses, plays a critical role in many robotic tasks such as robot-assisted living. Due to the adaptive and often reflective texture of such objects, traditional tracking…
ObjectObject TrackingTransparent objectsNeTO: Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing
We present a novel method called NeTO, for capturing the 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited fo…
3D geometryTransparent objectsTransNet: Transparent Object Manipulation Through Category-Level Pose Estimation
Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even…
Depth CompletionObjectPose EstimationSurface Normal Estimation+1FDCT: Fast Depth Completion for Transparent Objects
Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to m…
Autonomous DrivingDepth CompletionDepth EstimationPose Estimation+1TRansPose: Large-Scale Multispectral Dataset for Transparent Object
Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth…
ObjectTransparent objectsInternal-External Boundary Attention Fusion for Glass Surface Segmentation
Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces…
Semantic SegmentationTransparent objectsRDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent materials frequently elude detection by …
Depth CompletionMissing ValuesTransparent objectsSegment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of computer vision, SAM (Segment Anything M…
SegmentationSemantic SegmentationTransparent objectsNEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects
We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model ca…
Image MattingNeural RenderingObjectTransparent objectsNeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing
We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for g…
3D geometryTransparent objectsTransMatting: Tri-token Equipped Transformer Model for Image Matting
Image matting aims to predict alpha values of elaborate uncertainty areas of natural images, like hairs, smoke, and spider web. However, existing methods perform poorly when faced with highly transparent foreground objec…
Image MattingTransparent objectsMVTrans: Multi-View Perception of Transparent Objects
Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings. Existing methods utilize RGB-D or stereo inputs to handle a subset of perception tasks in…
Dataset GenerationDepth EstimationObjectobject-detection+5Consistent Depth Prediction for Transparent Object Reconstruction from RGB-D Camera
Transparent objects are commonly seen in indoor scenes but are hard to estimate. Currently, commercial depth cameras face difficulties in estimating the depth of transparent objects due to the light reflection and re…
Depth EstimationDepth PredictionObject ReconstructionPrediction+1Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant ligh…
ClassificationDataset GenerationPositionTransparent objectsGrasping the Inconspicuous
Transparent objects are common in day-to-day life and hence find many applications that require robot grasping. Many solutions toward object grasping exist for non-transparent objects. However, due to the unique visual p…
6D Pose EstimationObjectPose EstimationTransparent objectsStereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS
Most existing methods for category-level pose estimation rely on object point clouds. However, when considering transparent objects, depth cameras are usually not able to capture meaningful data, resulting in point cloud…
ObjectPose EstimationTransparent objectsData-Driven Computational Imaging for Scientific Discovery
In computational imaging, hardware for signal sampling and software for object reconstruction are designed in tandem for improved capability. Examples of such systems include computed tomography (CT), magnetic resonance …
Computed Tomography (CT)Object Reconstructionscientific discoverySuper-Resolution+1