GSCNet with FDAM and SGCM modules plus the URTF benchmark improves fine-grained semantic segmentation on unaligned UAV RGBT images.
Deep Image Homography Estimation
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
abstract
We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach.
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cs.CV 6verdicts
UNVERDICTED 6representative citing papers
SA-Homo introduces a hierarchical scale-adaptive homography estimation framework with SDBM, MLAC, CSMB, and IHERM modules plus the HMSA dataset that claims robust performance under up to 8x scale discrepancies.
CamFlow+ is a hybrid-basis framework for 2D camera motion estimation that relaxes single-plane constraints using physical, stochastic, and depth-derived bases, with evaluation on a masked camera-motion benchmark and stabilization applications.
Uni-Hand forecasts 2D/3D hand waypoints, head motion, and contact states in egocentric views using vision-language fusion and dual-branch diffusion, with new benchmarks for downstream robotics and action tasks.
Homographic navigation uses homography-driven synthetic data generation and a two-pass keypoint model with Stable Warp training to achieve accurate planar alignment from minimal supervision.
A cGAN with U-Net generator and PatchGAN discriminator learns radiometric normalization from Chandrayaan-2 TMC imagery to LROC WAC reference, yielding improved tonal uniformity and fewer seams than histogram matching.
citing papers explorer
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Graph-based Semantic Calibration Network for Unaligned UAV RGBT Image Semantic Segmentation and A Large-scale Benchmark
GSCNet with FDAM and SGCM modules plus the URTF benchmark improves fine-grained semantic segmentation on unaligned UAV RGBT images.
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SA-Homo: Scale Adaptive Homography Estimation for Scale Variation Scenarios
SA-Homo introduces a hierarchical scale-adaptive homography estimation framework with SDBM, MLAC, CSMB, and IHERM modules plus the HMSA dataset that claims robust performance under up to 8x scale discrepancies.
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CamFlow+: Hybrid Motion Bases for 2D Camera Motion Estimation with Stabilization Applications
CamFlow+ is a hybrid-basis framework for 2D camera motion estimation that relaxes single-plane constraints using physical, stochastic, and depth-derived bases, with evaluation on a masked camera-motion benchmark and stabilization applications.
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Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views
Uni-Hand forecasts 2D/3D hand waypoints, head motion, and contact states in egocentric views using vision-language fusion and dual-branch diffusion, with new benchmarks for downstream robotics and action tasks.
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Homographic Navigation: Geometry-Driven Camera Guidance for Deterministic Planar Capture
Homographic navigation uses homography-driven synthetic data generation and a two-pass keypoint model with Stable Warp training to achieve accurate planar alignment from minimal supervision.
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Towards Seamless Lunar Mosaics: Deep Radiometric Normalization for Cross-Sensor Orbital Imagery Using Chandrayaan-2 TMC Data
A cGAN with U-Net generator and PatchGAN discriminator learns radiometric normalization from Chandrayaan-2 TMC imagery to LROC WAC reference, yielding improved tonal uniformity and fewer seams than histogram matching.