Search Results for author:
Found 22 papers, 11 papers with code
A Multi-Task Network for Joint Specular Highlight Detection and Removal
Specular highlight detection and removal are fundamental and challenging tasks.
PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval
Point cloud based retrieval for place recognition is an emerging problem in vision field.
Ranked #9 on
3D Place Recognition
on Oxford RobotCar Dataset
(AR@1% metric)
Detail Preserved Point Cloud Completion via Separated Feature Aggregation
In this work, instead of using a global feature to recover the whole complete surface, we explore the functionality of multi-level features and aggregate different features to represent the known part and the missing part separately.
Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context
A perennial problem in structure from motion (SfM) is visual ambiguity posed by repetitive structures.
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
To our best knowledge, we are the first one to explore residual and illumination for shadow removal.
Document Image Shadow Removal Guided by Color-Aware Background
In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document.
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
This paper aims to remove specular highlights from a single object-level image.
Luminance Attentive Networks for HDR Image and Panorama Reconstruction
Based on this observation, we propose a novel normalization method called " HDR calibration " for HDR images stored in relative luminance, calibrating HDR images into a similar luminance scale according to the LDR images.
CANet: A Context-Aware Network for Shadow Removal
In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces.
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset.
Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioning
With the well-designed Dual-GCN, we can make the linguistic transformer better understand the relationship between different objects in a single image and make full use of similar images as auxiliary information to generate a reasonable caption description for a single image.
Texture Mapping for 3D Reconstruction With RGB-D Sensor
Acquiring realistic texture details for 3D models is important in 3D reconstruction.
Enhancing Underexposed Photos using Perceptually Bidirectional Similarity
To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency.
VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling
In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images generated by Text-to-image Generative Adversarial Network (GAN) to improve the performance of image labeling.
ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal
In this paper we propose an attentive recurrent generative adversarial network (ARGAN) to detect and remove shadows in an image.
Generative Adversarial Network
Shadow Detection And Removal
+1
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation
Based on those constraints, a category-consistent and relativistic diverse conditional GAN (CRD-CGAN) is proposed to synthesize $K$ photo-realistic images simultaneously.
Deep Image-based Illumination Harmonization
Specifically, we firstly apply a physically-based rendering method to construct a large-scale, high-quality dataset (named IH) for our task, which contains various types of foreground objects and background scenes with different lighting conditions.
DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation
To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task.
NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction
Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity.
NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing
In this paper, we propose to leverage implicit Signed Distance Function (SDF) as surface representation, and optimize the SDF field via volume rendering with a self-occlusion aware refractive ray tracing.
Learning Long-Range Information with Dual-Scale Transformers for Indoor Scene Completion
Due to the limited resolution of 3D sensors and the inevitable mutual occlusion between objects, 3D scans of real scenes are commonly incomplete.
DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
To model the deformation more accurately, we propose to initialize an estimated 3D clothed human in the canonical space, as it is easier for deformation fields to learn from the clothed human than from SMPL.
