105 research outputs found
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
We address the problem of mesh reconstruction from live RGB-D video, assuming
a calibrated camera and poses provided externally (e.g., by a SLAM system). In
contrast to most existing approaches, we do not fuse depth measurements in a
volume but in a dense surfel cloud. We asynchronously (re)triangulate the
smoothed surfels to reconstruct a surface mesh. This novel approach enables to
maintain a dense surface representation of the scene during SLAM which can
quickly adapt to loop closures. This is possible by deforming the surfel cloud
and asynchronously remeshing the surface where necessary. The surfel-based
representation also naturally supports strongly varying scan resolution. In
particular, it reconstructs colors at the input camera's resolution. Moreover,
in contrast to many volumetric approaches, ours can reconstruct thin objects
since objects do not need to enclose a volume. We demonstrate our approach in a
number of experiments, showing that it produces reconstructions that are
competitive with the state-of-the-art, and we discuss its advantages and
limitations. The algorithm (excluding loop closure functionality) is available
as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Hybrid Scene Compression for Visual Localization
Localizing an image wrt. a 3D scene model represents a core task for many
computer vision applications. An increasing number of real-world applications
of visual localization on mobile devices, e.g., Augmented Reality or autonomous
robots such as drones or self-driving cars, demand localization approaches to
minimize storage and bandwidth requirements. Compressing the 3D models used for
localization thus becomes a practical necessity. In this work, we introduce a
new hybrid compression algorithm that uses a given memory limit in a more
effective way. Rather than treating all 3D points equally, it represents a
small set of points with full appearance information and an additional, larger
set of points with compressed information. This enables our approach to obtain
a more complete scene representation without increasing the memory
requirements, leading to a superior performance compared to previous
compression schemes. As part of our contribution, we show how to handle
ambiguous matches arising from point compression during RANSAC. Besides
outperforming previous compression techniques in terms of pose accuracy under
the same memory constraints, our compression scheme itself is also more
efficient. Furthermore, the localization rates and accuracy obtained with our
approach are comparable to state-of-the-art feature-based methods, while using
a small fraction of the memory.Comment: Published at CVPR 201
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud
representation of the scene that does not model the topology of the
environment. A 3D mesh instead offers a richer, yet lightweight, model.
Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks
triangulated by a VIO algorithm often results in a mesh that does not fit the
real scene. In order to regularize the mesh, previous approaches decouple state
estimation from the 3D mesh regularization step, and either limit the 3D mesh
to the current frame or let the mesh grow indefinitely. We propose instead to
tightly couple mesh regularization and state estimation by detecting and
enforcing structural regularities in a novel factor-graph formulation. We also
propose to incrementally build the mesh by restricting its extent to the
time-horizon of the VIO optimization; the resulting 3D mesh covers a larger
portion of the scene than a per-frame approach while its memory usage and
computational complexity remain bounded. We show that our approach successfully
regularizes the mesh, while improving localization accuracy, when structural
regularities are present, and remains operational in scenes without
regularities.Comment: 7 pages, 5 figures, ICRA accepte
Understanding the Limitations of CNN-based Absolute Camera Pose Regression
Visual localization is the task of accurate camera pose estimation in a known
scene. It is a key problem in computer vision and robotics, with applications
including self-driving cars, Structure-from-Motion, SLAM, and Mixed Reality.
Traditionally, the localization problem has been tackled using 3D geometry.
Recently, end-to-end approaches based on convolutional neural networks have
become popular. These methods learn to directly regress the camera pose from an
input image. However, they do not achieve the same level of pose accuracy as 3D
structure-based methods. To understand this behavior, we develop a theoretical
model for camera pose regression. We use our model to predict failure cases for
pose regression techniques and verify our predictions through experiments. We
furthermore use our model to show that pose regression is more closely related
to pose approximation via image retrieval than to accurate pose estimation via
3D structure. A key result is that current approaches do not consistently
outperform a handcrafted image retrieval baseline. This clearly shows that
additional research is needed before pose regression algorithms are ready to
compete with structure-based methods.Comment: Initial version of a paper accepted to CVPR 201
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
Efficient 2D-3D Matching for Multi-Camera Visual Localization
Visual localization, i.e., determining the position and orientation of a
vehicle with respect to a map, is a key problem in autonomous driving. We
present a multicamera visual inertial localization algorithm for large scale
environments. To efficiently and effectively match features against a pre-built
global 3D map, we propose a prioritized feature matching scheme for
multi-camera systems. In contrast to existing works, designed for monocular
cameras, we (1) tailor the prioritization function to the multi-camera setup
and (2) run feature matching and pose estimation in parallel. This
significantly accelerates the matching and pose estimation stages and allows us
to dynamically adapt the matching efforts based on the surrounding environment.
In addition, we show how pose priors can be integrated into the localization
system to increase efficiency and robustness. Finally, we extend our algorithm
by fusing the absolute pose estimates with motion estimates from a multi-camera
visual inertial odometry pipeline (VIO). This results in a system that provides
reliable and drift-less pose estimation. Extensive experiments show that our
localization runs fast and robust under varying conditions, and that our
extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure
Night-to-Day Image Translation for Retrieval-based Localization
Visual localization is a key step in many robotics pipelines, allowing the
robot to (approximately) determine its position and orientation in the world.
An efficient and scalable approach to visual localization is to use image
retrieval techniques. These approaches identify the image most similar to a
query photo in a database of geo-tagged images and approximate the query's pose
via the pose of the retrieved database image. However, image retrieval across
drastically different illumination conditions, e.g. day and night, is still a
problem with unsatisfactory results, even in this age of powerful neural
models. This is due to a lack of a suitably diverse dataset with true
correspondences to perform end-to-end learning. A recent class of neural models
allows for realistic translation of images among visual domains with relatively
little training data and, most importantly, without ground-truth pairings. In
this paper, we explore the task of accurately localizing images captured from
two traversals of the same area in both day and night. We propose ToDayGAN - a
modified image-translation model to alter nighttime driving images to a more
useful daytime representation. We then compare the daytime and translated night
images to obtain a pose estimate for the night image using the known 6-DOF
position of the closest day image. Our approach improves localization
performance by over 250% compared the current state-of-the-art, in the context
of standard metrics in multiple categories.Comment: Published in ICRA 201
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.Comment: In Proc. CVPR 201
- âŠ