Search Results for author:
Found 54 papers, 18 papers with code
Improving Referring Image Segmentation using Vision-Aware Text Features
Our method involves using CLIP to derive a CLIP Prior that integrates an object-centric visual heatmap with text description, which can be used as the initial query in DETR-based architecture for the segmentation task.
OmniGS: Omnidirectional Gaussian Splatting for Fast Radiance Field Reconstruction using Omnidirectional Images
In this paper, we present OmniGS, a novel omnidirectional Gaussian splatting system, to take advantage of omnidirectional images for fast radiance field reconstruction.
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
This is enabled by deformable attention mechanism, where the keys and values capturing the memory of a video sequence in the attention module have flexible locations updated across frames.
Ranked #7 on
Unsupervised Video Object Segmentation
on DAVIS 2016 val
(using extra training data)
Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study
In this study, we carry out the preliminary and comprehensive case study of utilizing GPT-4V for marine analysis.
Leveraging Open-Vocabulary Diffusion to Camouflaged Instance Segmentation
Such cross-domain representations are desirable in segmenting camouflaged objects where visual cues are subtle to distinguish the objects from the background, especially in segmenting novel objects which are not seen in training.
Advances in 3D Neural Stylization: A Survey
Building on such taxonomy, our survey first revisits the background of neural stylization on 2D images, and then provides in-depth discussions on recent neural stylization methods for 3D data, where we also provide a mini-benchmark on artistic stylization methods.
360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries
We propose a virtual camera approach to generate lower-FoV query frames from 360$^\circ$ images, which ensures a fair comparison of performance among different query types in visual localization tasks.
Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras
In addition to actively densifying hyper primitives based on geometric features, we further introduce a Gaussian-Pyramid-based training method to progressively learn multi-level features, enhancing photorealistic mapping performance.
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV).
Ranked #1 on
Semantic Segmentation
on LaRS
Test-Time Augmentation for 3D Point Cloud Classification and Segmentation
We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively.
MarineGPT: Unlocking Secrets of Ocean to the Public
Large language models (LLMs), such as ChatGPT/GPT-4, have proven to be powerful tools in promoting the user experience as an AI assistant.
CoralVOS: Dataset and Benchmark for Coral Video Segmentation
We perform experiments on our CoralVOS dataset, including 6 recent state-of-the-art video object segmentation (VOS) algorithms.
Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates
Specifically, to insert a new foreground object represented by a set of multi-view images into a background radiance field, we use a text-to-image diffusion model to learn and generate combined images that fuse the object of interest into the given background across views.
Locally Stylized Neural Radiance Fields
In this work, we propose a stylization framework for NeRF based on local style transfer.
360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking
360{\deg} images can provide an omnidirectional field of view which is important for stable and long-term scene perception.
Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration
We use this object layout to condition a generative adversarial network to synthesize images of an input scene.
MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding
Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors
In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network.
Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval
It is important to remember that domain specific data may be noisier (e. g., endoscopic or underwater videos) and often require more experienced users for effective search.
Ranked #1 on
Retrieval
on MVK
Time-of-Day Neural Style Transfer for Architectural Photographs
Architectural photography is a genre of photography that focuses on capturing a building or structure in the foreground with dramatic lighting in the background.
360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360$^\circ$ Radiance Fields
Subsequently, we assign local radiance fields through an adaptive divide-and-conquer strategy based on the recovered geometry.
RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal Features
The key insight is simultaneously performing both tasks via learning of spatial and temporal features from a sequence of point clouds can leverage individual tasks, leading to improved overall performance.
Ranked #1 on
3D Human Reconstruction
on Dynamic FAUST
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene understanding tasks.
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval
The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}).
Neural Scene Decoration from a Single Photograph
In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration.
Dual-SLAM: A framework for robust single camera navigation
As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle.
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds
With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e. g., object recognition, semantic segmentation.
Global Context Aware Convolutions for 3D Point Cloud Understanding
We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive.
SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information
Fully-automatic execution is the ultimate goal for many Computer Vision applications.
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching.
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning.
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data.
Ranked #8 on
3D Semantic Segmentation
on DALES
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
Ranked #2 on
3D Instance Segmentation
on SceneNN
3D Instance Segmentation
3D Semantic Instance Segmentation
+3
Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data
Urban zoning enables various applications in land use analysis and urban planning.
Uncalibrated Photometric Stereo Under Natural Illumination
This paper presents a photometric stereo method that works with unknown natural illuminations without any calibration object.
Self-Calibrating Polarising Radiometric Calibration
We present a self-calibrating polarising radiometric calibration method.
Real-time Progressive 3D Semantic Segmentation for Indoor Scene
The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation.
Pointwise Convolutional Neural Networks
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently.
Radiometric Calibration for Internet Photo Collections
Radiometrically calibrating the images from Internet photo collections brings photometric analysis from lab data to big image data in the wild, but conventional calibration methods cannot be directly applied to such image data.
GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching.
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data.
A Field Model for Repairing 3D Shapes
The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes.
A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo
Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions.
Towards Building an RGBD-M Scanner
We present a portable device to capture both shape and reflectance of an indoor scene.
Segmentation Rectification for Video Cutout via One-Class Structured Learning
Comprehensive experiments on both RGB and RGB-D data demonstrate that our simple and effective method significantly outperforms the segmentation propagation methods adopted in the state-of-the-art video cutout systems, and the results also suggest the potential usefulness of our method in image cutout system.
A Closed-Form Solution to Tensor Voting: Theory and Applications
We prove a closed-form solution to tensor voting (CFTV): given a point set in any dimensions, our closed-form solution provides an exact, continuous and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation.
Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning
We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera.
An MRF-Poselets Model for Detecting Highly Articulated Humans
The problem of human detection is then formulated as maximum a posteriori (MAP) estimation in the MRF model.
Superpixelizing Binary MRF for Image Labeling Problems
Superpixels have become prevalent in computer vision.
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
We propose a novel compact linear programming (LP) relaxation for binary sub-modular MRF in the context of object segmentation.
Shading-Based Shape Refinement of RGB-D Images
We present a shading-based shape refinement algorithm which uses a noisy, incomplete depth map from Kinect to help resolve ambiguities in shape-from-shading.
