21,563 research outputs found
Pixelwise Instance Segmentation with a Dynamically Instantiated Network
Semantic segmentation and object detection research have recently achieved
rapid progress. However, the former task has no notion of different instances
of the same object, and the latter operates at a coarse, bounding-box level. We
propose an Instance Segmentation system that produces a segmentation map where
each pixel is assigned an object class and instance identity label. Most
approaches adapt object detectors to produce segments instead of boxes. In
contrast, our method is based on an initial semantic segmentation module, which
feeds into an instance subnetwork. This subnetwork uses the initial
category-level segmentation, along with cues from the output of an object
detector, within an end-to-end CRF to predict instances. This part of our model
is dynamically instantiated to produce a variable number of instances per
image. Our end-to-end approach requires no post-processing and considers the
image holistically, instead of processing independent proposals. Therefore,
unlike some related work, a pixel cannot belong to multiple instances.
Furthermore, far more precise segmentations are achieved, as shown by our
state-of-the-art results (particularly at high IoU thresholds) on the Pascal
VOC and Cityscapes datasets.Comment: CVPR 201
Panoptic Segmentation
We propose and study a task we name panoptic segmentation (PS). Panoptic
segmentation unifies the typically distinct tasks of semantic segmentation
(assign a class label to each pixel) and instance segmentation (detect and
segment each object instance). The proposed task requires generating a coherent
scene segmentation that is rich and complete, an important step toward
real-world vision systems. While early work in computer vision addressed
related image/scene parsing tasks, these are not currently popular, possibly
due to lack of appropriate metrics or associated recognition challenges. To
address this, we propose a novel panoptic quality (PQ) metric that captures
performance for all classes (stuff and things) in an interpretable and unified
manner. Using the proposed metric, we perform a rigorous study of both human
and machine performance for PS on three existing datasets, revealing
interesting insights about the task. The aim of our work is to revive the
interest of the community in a more unified view of image segmentation.Comment: accepted to CVPR 201
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Recent deep learning models achieve impressive results on 3D scene analysis
tasks by operating directly on unstructured point clouds. A lot of progress was
made in the field of object classification and semantic segmentation. However,
the task of instance segmentation is less explored. In this work, we present
3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on
point clouds. Following the idea of previous proposal-free instance
segmentation approaches, our model learns a feature embedding and groups the
obtained feature space into semantic instances. Current point-based methods
scale linearly with the number of points by processing local sub-parts of a
scene individually. However, to perform instance segmentation by clustering,
globally consistent features are required. Therefore, we propose to combine
local point geometry with global context information from an intermediate
bird's-eye view representation.Comment: camera-ready version for GCPR '1
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive
deep learning framework for 3D object instance segmentation on point clouds.
SGPN uses a single network to predict point grouping proposals and a
corresponding semantic class for each proposal, from which we can directly
extract instance segmentation results. Important to the effectiveness of SGPN
is its novel representation of 3D instance segmentation results in the form of
a similarity matrix that indicates the similarity between each pair of points
in embedded feature space, thus producing an accurate grouping proposal for
each point. To the best of our knowledge, SGPN is the first framework to learn
3D instance-aware semantic segmentation on point clouds. Experimental results
on various 3D scenes show the effectiveness of our method on 3D instance
segmentation, and we also evaluate the capability of SGPN to improve 3D object
detection and semantic segmentation results. We also demonstrate its
flexibility by seamlessly incorporating 2D CNN features into the framework to
boost performance
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