624 research outputs found
Extracting real estate values of rental apartment floor plans using graph convolutional networks
Access graphs that indicate adjacency relationships from the perspective of
flow lines of rooms are extracted automatically from a large number of floor
plan images of a family-oriented rental apartment complex in Osaka Prefecture,
Japan, based on a recently proposed access graph extraction method with slight
modifications. We define and implement a graph convolutional network (GCN) for
access graphs and propose a model to estimate the real estate value of access
graphs as the floor plan value. The model, which includes the floor plan value
and hedonic method using other general explanatory variables, is used to
estimate rents and their estimation accuracies are compared. In addition, the
features of the floor plan that explain the rent are analyzed from the learned
convolution network. Therefore, a new model for comprehensively estimating the
value of real estate floor plans is proposed and validated. The results show
that the proposed method significantly improves the accuracy of rent estimation
compared to that of conventional models, and it is possible to understand the
specific spatial configuration rules that influence the value of a floor plan
by analyzing the learned GCN
Deep Learning based 3D Segmentation: A Survey
3D object segmentation is a fundamental and challenging problem in computer
vision with applications in autonomous driving, robotics, augmented reality and
medical image analysis. It has received significant attention from the computer
vision, graphics and machine learning communities. Traditionally, 3D
segmentation was performed with hand-crafted features and engineered methods
which failed to achieve acceptable accuracy and could not generalize to
large-scale data. Driven by their great success in 2D computer vision, deep
learning techniques have recently become the tool of choice for 3D segmentation
tasks as well. This has led to an influx of a large number of methods in the
literature that have been evaluated on different benchmark datasets. This paper
provides a comprehensive survey of recent progress in deep learning based 3D
segmentation covering over 150 papers. It summarizes the most commonly used
pipelines, discusses their highlights and shortcomings, and analyzes the
competitive results of these segmentation methods. Based on the analysis, it
also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure
Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning
In this paper, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. We train a deep neural network for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. The second simpler multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep network. In the experiments, we first compare different architectures to select the best deep network for location estimation, and then compare the effects of the multi-goal reinforcement learning method to traditional reinforcement learning. The results show a significant improvement when multi-goal reinforcement learning is used. Furthermore, the results of the location estimator show that a deep network can learn and generalize in different environments using camera images with high accuracy in both position and orientation
Machine Learning in Robotic Navigation:Deep Visual Localization and Adaptive Control
The work conducted in this thesis contributes to the robotic navigation field by focusing on different machine learning solutions: supervised learning with (deep) neural networks, unsupervised learning, and reinforcement learning.First, we propose a semi-supervised machine learning approach that can dynamically update the robot controller's parameters using situational analysis through feature extraction and unsupervised clustering. The results show that the robot can adapt to the changes in its surroundings, resulting in a thirty percent improvement in navigation speed and stability.Then, we train multiple deep neural networks for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. We prepare two image-based localization datasets in 3D simulation and compare the results of a traditional multilayer perceptron, a stacked denoising autoencoder, and a convolutional neural network (CNN). The experiment results show that our proposed inception based CNNs without pooling layers perform very well in all the environments. Finally, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. The multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep CNNs. The results show a significant improvement when multi-goal reinforcement learning is used
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
Humans are able to form a complex mental model of the environment they move
in. This mental model captures geometric and semantic aspects of the scene,
describes the environment at multiple levels of abstractions (e.g., objects,
rooms, buildings), includes static and dynamic entities and their relations
(e.g., a person is in a room at a given time). In contrast, current robots'
internal representations still provide a partial and fragmented understanding
of the environment, either in the form of a sparse or dense set of geometric
primitives (e.g., points, lines, planes, voxels) or as a collection of objects.
This paper attempts to reduce the gap between robot and human perception by
introducing a novel representation, a 3D Dynamic Scene Graph(DSG), that
seamlessly captures metric and semantic aspects of a dynamic environment. A DSG
is a layered graph where nodes represent spatial concepts at different levels
of abstraction, and edges represent spatio-temporal relations among nodes. Our
second contribution is Kimera, the first fully automatic method to build a DSG
from visual-inertial data. Kimera includes state-of-the-art techniques for
visual-inertial SLAM, metric-semantic 3D reconstruction, object localization,
human pose and shape estimation, and scene parsing. Our third contribution is a
comprehensive evaluation of Kimera in real-life datasets and photo-realistic
simulations, including a newly released dataset, uHumans2, which simulates a
collection of crowded indoor and outdoor scenes. Our evaluation shows that
Kimera achieves state-of-the-art performance in visual-inertial SLAM, estimates
an accurate 3D metric-semantic mesh model in real-time, and builds a DSG of a
complex indoor environment with tens of objects and humans in minutes. Our
final contribution shows how to use a DSG for real-time hierarchical semantic
path-planning. The core modules in Kimera are open-source.Comment: 34 pages, 25 figures, 9 tables. arXiv admin note: text overlap with
arXiv:2002.0628
Learning to plan with uncertain topological maps
We train an agent to navigate in 3D environments using a hierarchical
strategy including a high-level graph based planner and a local policy. Our
main contribution is a data driven learning based approach for planning under
uncertainty in topological maps, requiring an estimate of shortest paths in
valued graphs with a probabilistic structure. Whereas classical symbolic
algorithms achieve optimal results on noise-less topologies, or optimal results
in a probabilistic sense on graphs with probabilistic structure, we aim to show
that machine learning can overcome missing information in the graph by taking
into account rich high-dimensional node features, for instance visual
information available at each location of the map. Compared to purely learned
neural white box algorithms, we structure our neural model with an inductive
bias for dynamic programming based shortest path algorithms, and we show that a
particular parameterization of our neural model corresponds to the Bellman-Ford
algorithm. By performing an empirical analysis of our method in simulated
photo-realistic 3D environments, we demonstrate that the inclusion of visual
features in the learned neural planner outperforms classical symbolic solutions
for graph based planning.Comment: ECCV 202
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos
Taking advantage of human pose data for understanding human activities has
attracted much attention these days. However, state-of-the-art pose estimators
struggle in obtaining high-quality 2D or 3D pose data due to occlusion,
truncation and low-resolution in real-world un-annotated videos. Hence, in this
work, we propose 1) a Selective Spatio-Temporal Aggregation mechanism, named
SST-A, that refines and smooths the keypoint locations extracted by multiple
expert pose estimators, 2) an effective weakly-supervised self-training
framework which leverages the aggregated poses as pseudo ground-truth instead
of handcrafted annotations for real-world pose estimation. Extensive
experiments are conducted for evaluating not only the upstream pose refinement
but also the downstream action recognition performance on four datasets, Toyota
Smarthome, NTU-RGB+D, Charades, and Kinetics-50. We demonstrate that the
skeleton data refined by our Pose-Refinement system (SSTA-PRS) is effective at
boosting various existing action recognition models, which achieves competitive
or state-of-the-art performance.Comment: WACV202
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