31,576 research outputs found
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for globally consistent dense 3D reconstruction of large-scale scenes.
Our approach uses a dense Visual RGB-D SLAM system that estimates the camera
motion in real-time on a CPU and refines it in a global pose graph
optimization. Consecutive RGB-D frames are locally fused into keyframes, which
are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the
GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a
novel keyframe re-integration strategy with reduced GPU-host streaming. We
demonstrate in an extensive quantitative evaluation that our method is up to
93% more runtime efficient compared to the state-of-the-art and requires
significantly less memory, with only negligible loss of surface quality.
Overall, our system requires only a single GPU and allows for real-time surface
correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for globally consistent dense 3D reconstruction of large-scale scenes.
Our approach uses a dense Visual RGB-D SLAM system that estimates the camera
motion in real-time on a CPU and refines it in a global pose graph
optimization. Consecutive RGB-D frames are locally fused into keyframes, which
are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the
GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a
novel keyframe re-integration strategy with reduced GPU-host streaming. We
demonstrate in an extensive quantitative evaluation that our method is up to
93% more runtime efficient compared to the state-of-the-art and requires
significantly less memory, with only negligible loss of surface quality.
Overall, our system requires only a single GPU and allows for real-time surface
correction of large environments.Comment: British Machine Vision Conference (BMVC), London, September 201
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
Neural implicit representations have recently demonstrated compelling results
on dense Simultaneous Localization And Mapping (SLAM) but suffer from the
accumulation of errors in camera tracking and distortion in the reconstruction.
Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM
framework globally optimizing poses and 3D reconstruction in real-time. Robust
pose estimation is at its core, supported by efficient loop closing and online
full bundle adjustment, which optimize per frame by utilizing the learned
global geometry of the complete history of input frames. Simultaneously, we
update the implicit and continuous surface representation on-the-fly to ensure
global consistency of 3D reconstruction. Results on various synthetic and
real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art
approaches at tracking robustness and reconstruction accuracy. Furthermore,
GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.Comment: ICCV 2023. Code: https://github.com/youmi-zym/GO-SLAM - Project Page:
https://youmi-zym.github.io/projects/GO-SLAM
The neural network behind the eyes of a fly
With its regular, almost crystal-like structure, the fly optic lobe represents a particularly beautiful piece of nervous system, which consequently has attracted the attention of many researchers over the years. While the anatomy of the various cell types had been known from Golgi studies for long, their visual response properties could only recently be revealed thanks to the advent of cell-specific driver lines and genetically encoded indicators of neural activity. Furthermore, dense EM reconstruction of several columns of the fly optic lobe now provides information about the synaptic connections between the different cell types, and RNA sequencing sheds light on the transmitter systems and ionic conductances used for communication between them. Together with the molecular tools allowing for blocking and activating individual, genetically targeted cell types, the fly optic lobe can soon be one of the best-understood visual neuropils in neuroscience. In this review, we summarize what we have learned so far, and discuss the major difficulties that keep us from a complete understanding of visual processing in the fly optic lobe
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
NICP: Dense normal based point cloud registration
In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Learning to predict scene depth from RGB inputs is a challenging task both
for indoor and outdoor robot navigation. In this work we address unsupervised
learning of scene depth and robot ego-motion where supervision is provided by
monocular videos, as cameras are the cheapest, least restrictive and most
ubiquitous sensor for robotics.
Previous work in unsupervised image-to-depth learning has established strong
baselines in the domain. We propose a novel approach which produces higher
quality results, is able to model moving objects and is shown to transfer
across data domains, e.g. from outdoors to indoor scenes. The main idea is to
introduce geometric structure in the learning process, by modeling the scene
and the individual objects; camera ego-motion and object motions are learned
from monocular videos as input. Furthermore an online refinement method is
introduced to adapt learning on the fly to unknown domains.
The proposed approach outperforms all state-of-the-art approaches, including
those that handle motion e.g. through learned flow. Our results are comparable
in quality to the ones which used stereo as supervision and significantly
improve depth prediction on scenes and datasets which contain a lot of object
motion. The approach is of practical relevance, as it allows transfer across
environments, by transferring models trained on data collected for robot
navigation in urban scenes to indoor navigation settings. The code associated
with this paper can be found at https://sites.google.com/view/struct2depth.Comment: Thirty-Third AAAI Conference on Artificial Intelligence (AAAI'19
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