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Found 22 papers, 14 papers with code
SGFormer: Spherical Geometry Transformer for 360 Depth Estimation
Panoramic distortion poses a significant challenge in 360 depth estimation, particularly pronounced at the north and south poles.
Digging into contrastive learning for robust depth estimation with diffusion models
In this paper, we propose a novel robust depth estimation method called D4RD, featuring a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments.
Eliminating Warping Shakes for Unsupervised Online Video Stitching
In this paper, we retarget video stitching to an emerging issue, named warping shake, when extending image stitching to video stitching.
Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond
To break this bottleneck, we propose the coupled thin-plate spline model (CoupledTPS), which iteratively couples multiple TPS with limited control points into a more flexible and powerful transformation.
360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception
Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results as the compression process often muddles the semantics between various planes.
WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions
In this paper, we propose WeatherDepth, a self-supervised robust depth estimation model with curriculum contrastive learning, to tackle performance degradation in complex weather conditions.
GAFlow: Incorporating Gaussian Attention into Optical Flow
Moreover, for reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM) which not only inherits properties from Gaussian distribution to instinctively revolve around the neighbor fields of each point but also is empowered to put the emphasis on contextually related regions during matching.
Deep Learning for Camera Calibration and Beyond: A Survey
In this paper, we provide a comprehensive survey of learning-based camera calibration techniques, by analyzing their strengths and limitations.
Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness
Based on the Manhattan World assumption, most existing indoor layout estimation schemes focus on recovering layouts from vertically compressed 1D sequences.
Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision
In this paper, we propose the first unsupervised omnidirectional MVS framework based on multiple fisheye images.
Parallax-Tolerant Unsupervised Deep Image Stitching
First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion.
RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning
In this work, we explore constructing a win-win representation on both content and boundary by contributing a new learning model, i. e., Rectangling Rectification Network (RecRecNet).
Neural Contourlet Network for Monocular 360 Depth Estimation
For a monocular 360 image, depth estimation is a challenging because the distortion increases along the latitude.
Ranked #8 on
Depth Estimation
on Stanford2D3D Panoramic
Deep Rotation Correction without Angle Prior
To this end, we leverage a neural network to predict the optical flows that can warp the tilted images to be perceptually horizontal.
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation
In this paper, we combine the two different representations and propose a novel 360{\deg} semantic segmentation solution from a complementary perspective.
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap
It comprises two modules: Dual-Cubemap Depth Estimation (DCDE) module and Boundary Revision (BR) module.
PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation
In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions.
Ranked #4 on
Depth Estimation
on Stanford2D3D Panoramic
Deep Rectangling for Image Stitching: A Learning Baseline
In this paper, we address these issues by proposing the first deep learning solution to image rectangling.
FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving
LiDAR sensors are widely used in autonomous driving due to the reliable 3D spatial information.
Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation
Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration.
Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images
Even compared with the supervised solutions, our image stitching quality is still preferred by users.
Learning Edge-Preserved Image Stitching from Large-Baseline Deep Homography
In this paper, we propose an image stitching learning framework, which consists of a large-baseline deep homography module and an edge-preserved deformation module.
