51 citations · 150 across the 26 of their papers we have counts for
28 papers
360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking
Huajian Huang, Yinzhe Xu, Yingshu Chen +1
360° images can provide an omnidirectional field of view which is important for stable and long-term scene perception. In this paper, we explore 360° images for visual object track…
Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration
Ka Chun Shum, Hong-Wing Pang, Binh-Son Hua +2
In this paper, we address the problem of conditional scene decoration for 360-degree images. Our method takes a 360-degree background photograph of an indoor scene and generates de…
MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding
Tan-Sang Ha, Hai Nguyen-Truong, Tuan-Anh Vu +1
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 amou…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…
PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors
Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen +1
In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on…
Time-of-Day Neural Style Transfer for Architectural Photographs
Yingshu Chen, Tuan-Anh Vu, Ka-Chun Shum +2
Architectural photography is a genre of photography that focuses on capturing a building or structure in the foreground with dramatic lighting in the background. Inspired by recent…
Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval
Quang-Trung Truong, Tuan-Anh Vu, Tan-Sang Ha +4
Effective analysis of unusual domain specific video collections represents an important practical problem, where state-of-the-art general purpose models still face limitations. Hen…
360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360 Radiance Fields
Huajian Huang, Yingshu Chen, Tianjia Zhang +1
Virtual tour among sparse 360 images is widely used while hindering smooth and immersive roaming experiences. The emergence of Neural Radiance Field (NeRF) has showcased si…
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
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…
RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal Features
Tuan-Anh Vu, Duc Thanh Nguyen, Binh-Son Hua +2
Object reconstruction from 3D point clouds has been a long-standing research problem in computer vision and computer graphics, and achieved impressive progress. However, reconstruc…
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval
Hao Ren, Ziqiang Zheng, Yang Wu +4
The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}). The zero-shot sk…
Neural Scene Decoration from a Single Photograph
Hong-Wing Pang, Yingshu Chen, Phuoc-Hieu Le +3
Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, dec…
Dual-SLAM: A framework for robust single camera navigation
Huajian Huang, Wen-Yan Lin, Siying Liu +2
SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position es…
Global Context Aware Convolutions for 3D Point Cloud Understanding
Zhiyuan Zhang, Binh-Son Hua, Wei Chen +2
Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point…
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds
Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen +1
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 re…
SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information
Jing Yu Koh, Duc Thanh Nguyen, Quang-Trung Truong +2
Fully-automatic execution is the ultimate goal for many Computer Vision applications. However, this objective is not always realistic in tasks associated with high failure costs, s…
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua +3
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network…
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua +2
Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentati…
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Zhiyuan Zhang, Binh-Son Hua, David W. Rosen +1
Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotatio…
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
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. While bei…
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Quang-Hieu Pham, Duc Thanh Nguyen, Binh-Son Hua +2
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images. However, their power has not been fully realised on several tasks in 3D space, e.g…
Real-time Progressive 3D Semantic Segmentation for Indoor Scene
Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen +1
The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation. In this paper, we propo…
Pointwise Convolutional Neural Networks
Binh-Son Hua, Minh-Khoi Tran, Sai-Kit Yeung
Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with conv…
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Duc Thanh Nguyen, Binh-Son Hua, Lap-Fai Yu +1
Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a…