8 research outputs found
Holistically-Attracted Wireframe Parsing
This paper presents a fast and parsimonious parsing method to accurately and
robustly detect a vectorized wireframe in an input image with a single forward
pass. The proposed method is end-to-end trainable, consisting of three
components: (i) line segment and junction proposal generation, (ii) line
segment and junction matching, and (iii) line segment and junction
verification. For computing line segment proposals, a novel exact dual
representation is proposed which exploits a parsimonious geometric
reparameterization for line segments and forms a holistic 4-dimensional
attraction field map for an input image. Junctions can be treated as the
"basins" in the attraction field. The proposed method is thus called
Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed
method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban
dataset. On both benchmarks, it obtains state-of-the-art performance in terms
of accuracy and efficiency. For example, on the Wireframe dataset, compared to
the previous state-of-the-art method L-CNN, it improves the challenging mean
structural average precision (msAP) by a large margin ( absolute
improvements) and achieves 29.5 FPS on single GPU ( relative
improvement). A systematic ablation study is performed to further justify the
proposed method.Comment: Accepted by CVPR 202
Efficient solutions to the relative pose of three calibrated cameras from four points using virtual correspondences
We study the challenging problem of estimating the relative pose of three
calibrated cameras. We propose two novel solutions to the notoriously difficult
configuration of four points in three views, known as the 4p3v problem. Our
solutions are based on the simple idea of generating one additional virtual
point correspondence in two views by using the information from the locations
of the four input correspondences in the three views. For the first solver, we
train a network to predict this point correspondence. The second solver uses a
much simpler and more efficient strategy based on the mean points of three
corresponding input points. The new solvers are efficient and easy to implement
since they are based on the existing efficient minimal solvers, i.e., the
well-known 5-point relative pose and the P3P solvers. The solvers achieve
state-of-the-art results on real data. The idea of solving minimal problems
using virtual correspondences is general and can be applied to other problems,
e.g., the 5-point relative pose problem. In this way, minimal problems can be
solved using simpler non-minimal solvers or even using sub-minimal samples
inside RANSAC.
In addition, we compare different variants of 4p3v solvers with the baseline
solver for the minimal configuration consisting of three triplets of points and
two points visible in two views. We discuss which configuration of points is
potentially the most practical in real applications
Minimal Solutions for Relative Pose with a Single Affine Correspondence
In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases. It is shown, that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and a least-squares solution, a closedform solution for unknown focal length and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets from the KITTI benchmark. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in
accuracy with the benefit of a reduced number of needed RANSAC iterations
Minimal Solutions for Relative Pose with a Single Affine Correspondence
In this paper we present four cases of minimal solutions for two-view
relative pose estimation by exploiting the affine transformation between
feature points and we demonstrate efficient solvers for these cases. It is
shown, that under the planar motion assumption or with knowledge of a vertical
direction, a single affine correspondence is sufficient to recover the relative
camera pose. The four cases considered are two-view planar relative motion for
calibrated cameras as a closed-form and a least-squares solution, a closed-form
solution for unknown focal length and the case of a known vertical direction.
These algorithms can be used efficiently for outlier detection within a RANSAC
loop and for initial motion estimation. All the methods are evaluated on both
synthetic data and real-world datasets from the KITTI benchmark. The
experimental results demonstrate that our methods outperform comparable
state-of-the-art methods in accuracy with the benefit of a reduced number of
needed RANSAC iterations.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
202