6 citations · 12 across the 17 of their papers we have counts for
18 papers
GeoPano: Towards Geometrically Accurate 3D Reconstruction from a Single Panorama
Jing OU, Zidong Cao, Liaoyuan Fan +7
Panoramic 3D reconstruction enables the holistic recovery of complete 360 scene geometry, offering significant benefits for robotics and AR/VR applications. However, direc…
Reinforcing Egocentric Spatial Perception in Multimodal Large Language Models via Ego Scene Augmentation
Chi Kit Wong, Ye Pan, Yuanhuiyi Lyu +6
Egocentric Visual Question Answering (VQA) has attracted widespread attention as an important task for enabling Multimodal Large Language Models (MLLMs) to interact with the real w…
Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond
Jing Ou, Zidong Cao, Yinrui Ren +6
While feed-forward 3D reconstruction models have advanced rapidly, they still exhibit degraded performance on panoramas due to spherical distortions. Moreover, existing panoramic 3…
SAP: Segment Any 4K Panorama
Lutao Jiang, Zidong Cao, Weikai Chen +14
Promptable instance segmentation is widely adopted in embodied and AR systems, yet the performance of foundation models trained on perspective imagery often degrades on 360° panora…
EventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation
Yinrui Ren, Jinjing Zhu, Kanghao Chen +8
Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approach for robust 3D perception in c…
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision
Hao Ai, Zidong Cao, Lin Wang
Omnidirectional image (ODI) data is captured with a field-of-view of 360x180, which is much wider than the pinhole cameras and captures richer surrounding environment details than…
PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation
Zidong Cao, Jinjing Zhu, Weiming Zhang +4
Recently, Depth Anything Models (DAMs) - a type of depth foundation models - have demonstrated impressive zero-shot capabilities across diverse perspective images. Despite its succ…
CRF360D: Monocular 360 Depth Estimation via Spherical Fully-Connected CRFs
Zidong Cao, Lin Wang
Monocular 360 depth estimation is challenging due to the inherent distortion of the equirectangular projection (ERP). This distortion causes a problem: spherical adjacent points ar…
Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality
Zidong Cao, Zhan Wang, Yexin Liu +4
Viewing omnidirectional images (ODIs) in virtual reality (VR) represents a novel form of media that provides immersive experiences for users to navigate and interact with digital c…
Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting
Hao Ai, Zidong Cao, Haonan Lu +6
360 images, with a field-of-view (FoV) of 180x360, provide immersive and realistic environments for emerging virtual reality (VR) applications, such as virtual tourism, where users…
Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping
Tongyan Hua, Haotian Bai, Zidong Cao +3
In this paper, we introduce Hi-Map, a novel monocular dense mapping approach based on Neural Radiance Field (NeRF). Hi-Map is exceptional in its capacity to achieve efficient and h…
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events
Tianbo Pan, Zidong Cao, Lin Wang
Monocular depth estimation is a crucial task to measure distance relative to a camera, which is important for applications, such as robot navigation and self-driving. Traditional f…
OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution
Zidong Cao, Hao Ai, Yan-Pei Cao +3
Omnidirectional images (ODIs) have become increasingly popular, as their large field-of-view (FoV) can offer viewers the chance to freely choose the view directions in immersive en…
FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM
Tongyan Hua, Haotian Bai, Zidong Cao +1
In this paper, we introduce FMapping, an efficient neural field mapping framework that facilitates the continuous estimation of a colorized point cloud map in real-time dense RGB S…
360 High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer
Zidong Cao, Hao Ai, Athanasios V. Vasilakos +1
To predict high-resolution (HR) omnidirectional depth map, existing methods typically leverage HR omnidirectional image (ODI) as the input via fully-supervised learning. However, i…
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Xu Zheng, Jinjing Zhu, Yexin Liu +3
The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular…
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions
Hao Ai, Zidong cao, Yan-pei Cao +2
Depth estimation from a monocular 360° image is a burgeoning problem owing to its holistic sensing of a scene. Recently, some methods, \eg, OmniFusion, have applied the tangent pro…
Deep Learning for Omnidirectional Vision: A Survey and New Perspectives
Hao Ai, Zidong Cao, Jinjing Zhu +3
Omnidirectional image (ODI) data is captured with a 360x180 field-of-view, which is much wider than the pinhole cameras and contains richer spatial information than the conventiona…