86 research outputs found
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Why Can’t Neural Networks Forecast Pandemics Better
Why can’t neural networks (NN) forecast better? In the major super-forecasting competitions, NN have typically under-performed when compared to traditional statistical methods. When they have performed well, the underlying methods have been ensembles of NN and statistical methods. Forecasting stock markets, medical, infrastructure dynamics, social activity or pandemics each have their own challenges. In this study, we evaluate the strengths of a collection of methods for forecasting pandemics such as Covid-19 using NN, statistical methods as well as parameterized mechanistic models. Forecasts of epidemics can inform public health response and decision making, so accurate forecasting is crucial for general public notification, timing and spatial targeting of intervention. We show that NN typically under-perform in forecasting Covid-19 active cases which can be attributed to the lack of training data which is common for forecasts. Our test data consists of the top ten countries for active Covid-19 cases early in the pandemic and is represented as a Time Series (TS). We found that Statistical methods outperform NN for most cases. Albeit, NN are still good pattern finders and we suggest that there are perhaps more productive ways other than purely
data driven models of using NN to help produce better forecasts
Foot Depth Map Point Cloud Completion using Deep Learning with Residual Blocks
Fit is extremely important in footwear as fit largely determines performanceand comfort. Current footwear fit estimation mainly usesonly shoe size, which is extremely limited in characterizing theshape of a foot or the shape of a shoe. 3D scanning presents asolution to this, where a foot shape can be captured and virtuallyfit with shoe models. Traditional 3D scanning techniques have theirown complications however, stemming from their need to collectviews covering all aspects of an object. In this work we explore adeep learning technique to compete a foot scan point cloud frominformation contained in a single depth map view. We examine thebenefits of implementing residual blocks in architectures for this application,and find that they can improve accuracies while reducingmodel size and training time
Motion Segmentation from a Moving Monocular Camera
Identifying and segmenting moving objects from a moving monocular camera is
difficult when there is unknown camera motion, different types of object
motions and complex scene structures. To tackle these challenges, we take
advantage of two popular branches of monocular motion segmentation approaches:
point trajectory based and optical flow based methods, by synergistically
fusing these two highly complementary motion cues at object level. By doing
this, we are able to model various complex object motions in different scene
structures at once, which has not been achieved by existing methods. We first
obtain object-specific point trajectories and optical flow mask for each common
object in the video, by leveraging the recent foundational models in object
recognition, segmentation and tracking. We then construct two robust affinity
matrices representing the pairwise object motion affinities throughout the
whole video using epipolar geometry and the motion information provided by
optical flow. Finally, co-regularized multi-view spectral clustering is used to
fuse the two affinity matrices and obtain the final clustering. Our method
shows state-of-the-art performance on the KT3DMoSeg dataset, which contains
complex motions and scene structures. Being able to identify moving objects
allows us to remove them for map building when using visual SLAM or SFM.Comment: Accepted by IROS 2023 Workshop on Robotic Perception And Mapping:
Frontier Vision and Learning Technique
Effects of Spatial Transformer Location on Segmentation Performance of a Dense Transformer Network
Semantic segmentation solves the task of labelling every pixel inan image with its class label, and remains an important unsolvedproblem. While significant work has gone into using deep learningto solve this problem, almost all the existing research uses methodsthat do not make modifications on spatial context considered for thepixel being labelled. Spatial information is an important cue in taskssuch as segmentation, reusing the same spatial span for every pixeland every label may not be the best approach. Spatial TransformerNetworks have shown promising results in improving classificationperformance of existing networks by allowing networks to activelymanipulate their input data to achieve better performance. Our workshows the benefit of incorporating Spatial Transformer Networksand their corresponding decoders into networks tailored to semanticsegmentation. Our experiments show an improvement in performanceover baseline networks when using networks augmentedwith Spatial Transformers
Diagnosing Cardiac Deformations using 3d Optical Flow
This paper explores the viability of applying 3D optical flow techniques on 3D heart sequences to diagnose cardiac abnormalities and disease. Tagged magnetic resonance imaging (TMRI) is a non-invasive method to visualize in vivo myocardium motion during a cardiac cycle. By tracking the 3D trajectories of tagged material points it is possible to construct a volumetric model of the heart. Specifically, we use generated meshless deformable models (MDM) which describe an object as a point cloud inside the object boundary. We extend the 2D least squares and regularization approaches of Lucas and Kanade to 3D in order capture the flow, specifically the contraction and expansion of various parts of the heart motion. Features are extracted from this flow and a rudimentary SVM is used to classify unhealthy hearts
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