Search Results for author:
Found 54 papers, 24 papers with code
PIE: Physics-inspired Low-light Enhancement
In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE.
FriendNet: Detection-Friendly Dehazing Network
In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions?
PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis
The DSE module, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points.
Cross-BERT for Point Cloud Pretraining
By performing cross-modal interaction, Cross-BERT can smoothly reconstruct the masked tokens during pretraining, leading to notable performance enhancements for downstream tasks.
HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation Methods
In particular, we propose a Fine-Grained Feature Fusion (FGFF) module to effectively capture fine texture feature information around teeth and surrounding regions, and use these features to fine-grain the feature map to enhance the clarity of teeth.
SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures.
Don't worry about mistakes! Glass Segmentation Network via Mistake Correction
The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information.
Joint Depth Estimation and Mixture of Rain Removal From a Single Image
In this study, we propose an effective image deraining paradigm for Mixture of rain REmoval, called DEMore-Net, which takes full account of the MOR effect.
Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval
Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
Self-supervised learning is attracting large attention in point cloud understanding.
ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer
Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies.
PointSmile: Point Self-supervised Learning via Curriculum Mutual Information
From the perspective of how-and-what-to-learn, PointSmile is designed to imitate human curriculum learning, i. e., starting with an easy curriculum and gradually increasing the difficulty of that curriculum.
RainDiffusion: When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining
Before answering it, we observe two major obstacles of diffusion models in real-world image deraining: the need for paired training data and the limited utilization of multi-scale rain patterns.
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds.
PointSee: Image Enhances Point Cloud
There is a trend to fuse multi-modal information for 3D object detection (3OD).
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information.
LBF:Learnable Bilateral Filter For Point Cloud Denoising
Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising.
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).
Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing
Semi-UFormer can well leverage both the real-world hazy images and their uncertainty guidance information.
PSFormer: Point Transformer for 3D Salient Object Detection
We propose PSFormer, an effective point transformer model for 3D salient object detection.
SPCNet: Stepwise Point Cloud Completion Network
We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios.
Contrastive Semantic-Guided Image Smoothing Network
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.
Geometric and Learning-based Mesh Denoising: A Comprehensive Survey
In this work, we provide a comprehensive review of the advances in mesh denoising, containing both traditional geometric approaches and recent learning-based methods.
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD).
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement.
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.
SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
The SO(3)-equivariant features communicate with RGB features to deduce the (missed) geometry for detecting keypoints of an object with the reflective surface from the depth channel.
UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner.
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion.
Ranked #2 on
Point Cloud Completion
on ShapeNet-ViPC
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.
ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs
Low-overlap regions between paired point clouds make the captured features very low-confidence, leading cutting edge models to point cloud registration with poor quality.
Dynamic Message Propagation Network for RGB-D Salient Object Detection
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects.
AGConv: Adaptive Graph Convolution on 3D Point Clouds
In this paper, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis.
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
To our knowledge, RSOD is the first quantitatively evaluated and graded snowy OD dataset.
Transformers in 3D Point Clouds: A Survey
To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classification, segmentation, and object detection.
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds
The shape of circle is one of fundamental geometric primitives of man-made engineering objects.
SAR-ShipNet: SAR-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-resolution Feature Fusion
This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network.
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.
Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking
Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances.
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks.
GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural Networks
For the first time, we optimize both positions and normals (i. e., dual domains) in a unified framework of GNN, and show the powerful inter-coordination between the dual domains.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.
Ranked #15 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Adaptive Graph Convolution for Point Cloud Analysis
Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect.
Ranked #10 on
3D Point Cloud Classification
on IntrA
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
Learning Calibrated-Guidance for Object Detection in Aerial Images
Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.
VENet: Voting Enhancement Network for 3D Object Detection
Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
We have validated our approach on four recognized datasets (three synthetic and one real-world).
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise.
