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Found 13 papers, 9 papers with code
Affine steerers for structured keypoint description
We propose a way to train deep learning based keypoint descriptors that makes them approximately equivariant for locally affine transformations of the image plane.
From 2D to 3D: AISG-SLA Visual Localization Challenge
To tackle these challenges, we organized the AISG-SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how AI can accurately extract camera pose data from 2D images in 3D space.
DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector
First, we find that DeDoDe keypoints tend to cluster together, which we fix by performing non-max suppression on the target distribution of the detector during training.
Steerers: A framework for rotation equivariant keypoint descriptors
Image keypoint descriptions that are discriminative and matchable over large changes in viewpoint are vital for 3D reconstruction.
Leveraging Cutting Edge Deep Learning Based Image Matching for Reconstructing a Large Scene from Sparse Images
We present the top ranked solution for the AISG-SLA Visual Localisation Challenge benchmark (IJCAI 2023), where the task is to estimate relative motion between images taken in sequence by a camera mounted on a car driving through an urban scene.
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
To train a descriptor, we maximize the mutual nearest neighbour objective over the keypoints with a separate network.
Investigating how ReLU-networks encode symmetries
These experiments are not only of interest for understanding how group equivariance is encoded in ReLU-networks, but they also give a new perspective on Entezari et al.'s permutation conjecture as we find that it is typically easier to merge a network with a group-transformed version of itself than merging two different networks.
RoMa: Robust Dense Feature Matching
The aim is to learn a robust model, i. e., a model able to match under challenging real-world changes.
In Search of Projectively Equivariant Networks
Equivariance of linear neural network layers is well studied.
A case for using rotation invariant features in state of the art feature matchers
The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations.
Rigidity Preserving Image Transformations and Equivariance in Perspective
We characterize the class of image plane transformations which realize rigid camera motions and call these transformations `rigidity preserving'.
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds
In this paper, we are concerned with rotation equivariance on 2D point cloud data.
Azimuthal Rotational Equivariance in Spherical CNNs
In this work, we analyze linear operators from $L^2(S^2) \rightarrow L^2(S^2)$ which are equivariant to azimuthal rotations, that is, rotations around the z-axis.
