Search Results for author:
Found 21 papers, 12 papers with code
Efficient 2D-3D Matching for Multi-Camera Visual Localization
This results in a system that provides reliable and drift-less pose estimations for high speed autonomous driving.
Robotics
Robust Dense Mapping for Large-Scale Dynamic Environments
We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars.
Deep Flow Collaborative Network for Online Visual Tracking
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network.
Visual Privacy Protection via Mapping Distortion
To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios.
Self-Supervised Linear Motion Deblurring
Motion blurry images challenge many computer vision algorithms, e. g, feature detection, motion estimation, or object recognition.
Deep Shutter Unrolling Network
The dense displacement field from a rolling shutter image to its corresponding global shutter image is estimated via a motion estimation network.
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
MBA-VO: Motion Blur Aware Visual Odometry
Motion blur is one of the major challenges remaining for visual odometry methods.
SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation
The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance.
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations
In this work, we propose an effective weakly-supervised video semantic segmentation pipeline with click annotations, called WeClick, for saving laborious annotating effort by segmenting an instance of the semantic class with only a single click.
BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields
Our approach models the physical image formation process of a motion blurred image, and jointly learns the parameters of NeRF and recovers the camera motion trajectories during exposure time.
BALF: Simple and Efficient Blur Aware Local Feature Detector
Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization.
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
USB-NeRF is able to correct rolling shutter distortions and recover accurate camera motion trajectory simultaneously under the framework of NeRF, by modeling the physical image formation process of a RS camera.
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
Our approach enables the generation of controllable multi-view images and view-consistent 3D content.
Vector-Quantized Prompt Learning for Paraphrase Generation
Therefore, we present vector-quantized prompts as the cues to control the generation of pre-trained models.
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation
The main insight is that we exploit the images generated by a large pre-trained text-to-image diffusion model, to supervise the training of a text conditioned 3D generative adversarial network.
Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
Building upon our MVControl architecture, we employ a unique hybrid diffusion guidance method to direct the optimization process.
BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting
In this paper, we introduce a novel approach, named BAD-Gaussians (Bundle Adjusted Deblur Gaussian Splatting), which leverages explicit Gaussian representation and handles severe motion-blurred images with inaccurate camera poses to achieve high-quality scene reconstruction.
Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network
A new benchmark for cross-domain MAV detection is proposed based on the proposed dataset.
SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image
SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors.
DerainNeRF: 3D Scene Estimation with Adhesive Waterdrop Removal
When capturing images through the glass during rainy or snowy weather conditions, the resulting images often contain waterdrops adhered on the glass surface, and these waterdrops significantly degrade the image quality and performance of many computer vision algorithms.
