Neural Geometric Parser for Single Image Camera Calibration
- URL: http://arxiv.org/abs/2007.11855v2
- Date: Fri, 24 Jul 2020 02:16:03 GMT
- Title: Neural Geometric Parser for Single Image Camera Calibration
- Authors: Jinwoo Lee and Minhyuk Sung and Hyunjoon Lee and Junho Kim
- Abstract summary: We propose a neural geometric learning single image camera calibration for man-made scenes.
Our approach considers both semantic and geometric cues, resulting in significant accuracy improvement.
The experimental results reveal that the performance of our neural approach is significantly higher than that of existing state-of-the-art camera calibration techniques.
- Score: 17.393543270903653
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a neural geometric parser learning single image camera calibration
for man-made scenes. Unlike previous neural approaches that rely only on
semantic cues obtained from neural networks, our approach considers both
semantic and geometric cues, resulting in significant accuracy improvement. The
proposed framework consists of two networks. Using line segments of an image as
geometric cues, the first network estimates the zenith vanishing point and
generates several candidates consisting of the camera rotation and focal
length. The second network evaluates each candidate based on the given image
and the geometric cues, where prior knowledge of man-made scenes is used for
the evaluation. With the supervision of datasets consisting of the horizontal
line and focal length of the images, our networks can be trained to estimate
the same camera parameters. Based on the Manhattan world assumption, we can
further estimate the camera rotation and focal length in a weakly supervised
manner. The experimental results reveal that the performance of our neural
approach is significantly higher than that of existing state-of-the-art camera
calibration techniques for single images of indoor and outdoor scenes.
Related papers
- Learning Robust Multi-Scale Representation for Neural Radiance Fields
from Unposed Images [65.41966114373373]
We present an improved solution to the neural image-based rendering problem in computer vision.
The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
arXiv Detail & Related papers (2023-11-08T08:18:23Z) - Inverting the Imaging Process by Learning an Implicit Camera Model [73.81635386829846]
This paper proposes a novel implicit camera model which represents the physical imaging process of a camera as a deep neural network.
We demonstrate the power of this new implicit camera model on two inverse imaging tasks.
arXiv Detail & Related papers (2023-04-25T11:55:03Z) - Multi-task Learning for Camera Calibration [3.274290296343038]
We present a unique method for predicting intrinsic (principal point offset and focal length) and extrinsic (baseline, pitch, and translation) properties from a pair of images.
By reconstructing the 3D points using a camera model neural network and then using the loss in reconstruction to obtain the camera specifications, this innovative camera projection loss (CPL) method allows us that the desired parameters should be estimated.
arXiv Detail & Related papers (2022-11-22T17:39:31Z) - Fusing Convolutional Neural Network and Geometric Constraint for
Image-based Indoor Localization [4.071875179293035]
This paper proposes a new image-based localization framework that explicitly localizes the camera/robot.
The camera is localized using a single or few observed images and training images with 6-degree-of-freedom pose labels.
Experiments on simulation and real data sets demonstrate the efficiency of our proposed method.
arXiv Detail & Related papers (2022-01-05T02:04:41Z) - A singular Riemannian geometry approach to Deep Neural Networks II.
Reconstruction of 1-D equivalence classes [78.120734120667]
We build the preimage of a point in the output manifold in the input space.
We focus for simplicity on the case of neural networks maps from n-dimensional real spaces to (n - 1)-dimensional real spaces.
arXiv Detail & Related papers (2021-12-17T11:47:45Z) - Back to the Feature: Learning Robust Camera Localization from Pixels to
Pose [114.89389528198738]
We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model.
The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching.
arXiv Detail & Related papers (2021-03-16T17:40:12Z) - Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion [51.19260542887099]
We show that self-supervision can be used to learn accurate depth and ego-motion estimation without prior knowledge of the camera model.
Inspired by the geometric model of Grossberg and Nayar, we introduce Neural Ray Surfaces (NRS), convolutional networks that represent pixel-wise projection rays.
We demonstrate the use of NRS for self-supervised learning of visual odometry and depth estimation from raw videos obtained using a wide variety of camera systems.
arXiv Detail & Related papers (2020-08-15T02:29:13Z) - 3D Scene Geometry-Aware Constraint for Camera Localization with Deep
Learning [11.599633757222406]
Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods.
In this work, we propose a compact network for absolute camera pose regression.
Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents.
arXiv Detail & Related papers (2020-05-13T04:15:14Z) - Consistent Video Depth Estimation [57.712779457632024]
We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video.
We leverage a conventional structure-from-motion reconstruction to establish geometric constraints on pixels in the video.
Our algorithm is able to handle challenging hand-held captured input videos with a moderate degree of dynamic motion.
arXiv Detail & Related papers (2020-04-30T17:59:26Z) - On Localizing a Camera from a Single Image [9.049593493956008]
We show that it is possible to estimate the location of a camera from a single image taken by the camera.
We show that, using a judicious combination of projective geometry, neural networks, and crowd-sourced annotations from human workers, it is possible to position 95% of the images in our test data set to within 12 m.
arXiv Detail & Related papers (2020-03-24T05:09:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.