714 research outputs found
Active Image-based Modeling with a Toy Drone
Image-based modeling techniques can now generate photo-realistic 3D models
from images. But it is up to users to provide high quality images with good
coverage and view overlap, which makes the data capturing process tedious and
time consuming. We seek to automate data capturing for image-based modeling.
The core of our system is an iterative linear method to solve the multi-view
stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our
fast MVS algorithm enables online model reconstruction and quality assessment
to determine the NBVs on the fly. We test our system with a toy unmanned aerial
vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that
our system improves the efficiency of data acquisition and ensures the
completeness of the final model.Comment: To be published on International Conference on Robotics and
Automation 2018, Brisbane, Australia. Project Page:
https://huangrui815.github.io/active-image-based-modeling/ The author's
personal page: http://www.sfu.ca/~rha55
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
Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field
In this paper, we address the problem of simultaneous relighting and novel
view synthesis of a complex scene from multi-view images with a limited number
of light sources. We propose an analysis-synthesis approach called Relit-NeuLF.
Following the recent neural 4D light field network (NeuLF), Relit-NeuLF first
leverages a two-plane light field representation to parameterize each ray in a
4D coordinate system, enabling efficient learning and inference. Then, we
recover the spatially-varying bidirectional reflectance distribution function
(SVBRDF) of a 3D scene in a self-supervised manner. A DecomposeNet learns to
map each ray to its SVBRDF components: albedo, normal, and roughness. Based on
the decomposed BRDF components and conditioning light directions, a RenderNet
learns to synthesize the color of the ray. To self-supervise the SVBRDF
decomposition, we encourage the predicted ray color to be close to the
physically-based rendering result using the microfacet model. Comprehensive
experiments demonstrate that the proposed method is efficient and effective on
both synthetic data and real-world human face data, and outperforms the
state-of-the-art results. We publicly released our code on GitHub. You can find
it here: https://github.com/oppo-us-research/RelitNeuLFComment: 10 page
- …