54,934 research outputs found
Zero-Level-Set Encoder for Neural Distance Fields
Neural shape representation generally refers to representing 3D geometry
using neural networks, e.g., to compute a signed distance or occupancy value at
a specific spatial position. Previous methods tend to rely on the auto-decoder
paradigm, which often requires densely-sampled and accurate signed distances to
be known during training and testing, as well as an additional optimization
loop during inference. This introduces a lot of computational overhead, in
addition to having to compute signed distances analytically, even during
testing. In this paper, we present a novel encoder-decoder neural network for
embedding 3D shapes in a single forward pass. Our architecture is based on a
multi-scale hybrid system incorporating graph-based and voxel-based components,
as well as a continuously differentiable decoder. Furthermore, the network is
trained to solve the Eikonal equation and only requires knowledge of the
zero-level set for training and inference. Additional volumetric samples can be
generated on-the-fly, and incorporated in an unsupervised manner. This means
that in contrast to most previous work, our network is able to output valid
signed distance fields without explicit prior knowledge of non-zero distance
values or shape occupancy. In other words, our network computes approximate
solutions to the boundary-valued Eikonal equation. It also requires only a
single forward pass during inference, instead of the common latent code
optimization. We further propose a modification of the loss function in case
that surface normals are not well defined, e.g., in the context of
non-watertight surface-meshes and non-manifold geometry. We finally demonstrate
the efficacy, generalizability and scalability of our method on datasets
consisting of deforming 3D shapes, single class encoding and multiclass
encoding, showcasing a wide range of possible applications
Place Categorization and Semantic Mapping on a Mobile Robot
In this paper we focus on the challenging problem of place categorization and
semantic mapping on a robot without environment-specific training. Motivated by
their ongoing success in various visual recognition tasks, we build our system
upon a state-of-the-art convolutional network. We overcome its closed-set
limitations by complementing the network with a series of one-vs-all
classifiers that can learn to recognize new semantic classes online. Prior
domain knowledge is incorporated by embedding the classification system into a
Bayesian filter framework that also ensures temporal coherence. We evaluate the
classification accuracy of the system on a robot that maps a variety of places
on our campus in real-time. We show how semantic information can boost robotic
object detection performance and how the semantic map can be used to modulate
the robot's behaviour during navigation tasks. The system is made available to
the community as a ROS module
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
We consider the problem of segmenting a biomedical image into anatomical
regions of interest. We specifically address the frequent scenario where we
have no paired training data that contains images and their manual
segmentations. Instead, we employ unpaired segmentation images to build an
anatomical prior. Critically these segmentations can be derived from imaging
data from a different dataset and imaging modality than the current task. We
introduce a generative probabilistic model that employs the learned prior
through a convolutional neural network to compute segmentations in an
unsupervised setting. We conducted an empirical analysis of the proposed
approach in the context of structural brain MRI segmentation, using a
multi-study dataset of more than 14,000 scans. Our results show that an
anatomical prior can enable fast unsupervised segmentation which is typically
not possible using standard convolutional networks. The integration of
anatomical priors can facilitate CNN-based anatomical segmentation in a range
of novel clinical problems, where few or no annotations are available and thus
standard networks are not trainable. The code is freely available at
http://github.com/adalca/neuron.Comment: Presented at CVPR 2018. IEEE CVPR proceedings pp. 9290-929
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