8,761 research outputs found
Joint segmentation of color and depth data based on splitting and merging driven by surface fitting
This paper proposes a segmentation scheme based on the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color, geometry and surface orientation information. Normalized cuts spectral clustering is then applied in order to recursively segment the scene in two parts thus obtaining an over-segmentation. This procedure is followed by a recursive merging stage where close segments belonging to the same object are joined together. At each step of both procedures a NURBS model is fitted on the computed segments and the accuracy of the fitting is used as a measure of the plausibility that a segment represents a single surface or object. By comparing the accuracy to the one at the previous step, it is possible to determine if each splitting or merging operation leads to a better scene representation and consequently whether to perform it or not. Experimental results show how the proposed method provides an accurate and reliable segmentation
Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting
We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
We present Im2Pano3D, a convolutional neural network that generates a dense
prediction of 3D structure and a probability distribution of semantic labels
for a full 360 panoramic view of an indoor scene when given only a partial
observation (<= 50%) in the form of an RGB-D image. To make this possible,
Im2Pano3D leverages strong contextual priors learned from large-scale synthetic
and real-world indoor scenes. To ease the prediction of 3D structure, we
propose to parameterize 3D surfaces with their plane equations and train the
model to predict these parameters directly. To provide meaningful training
supervision, we use multiple loss functions that consider both pixel level
accuracy and global context consistency. Experiments demon- strate that
Im2Pano3D is able to predict the semantics and 3D structure of the unobserved
scene with more than 56% pixel accuracy and less than 0.52m average distance
error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Depth map compression via 3D region-based representation
In 3D video, view synthesis is used to create new virtual views between
encoded camera views. Errors in the coding of the depth maps introduce
geometry inconsistencies in synthesized views. In this paper, a new 3D plane
representation of the scene is presented which improves the performance of
current standard video codecs in the view synthesis domain. Two image segmentation
algorithms are proposed for generating a color and depth segmentation.
Using both partitions, depth maps are segmented into regions without
sharp discontinuities without having to explicitly signal all depth edges. The
resulting regions are represented using a planar model in the 3D world scene.
This 3D representation allows an efficient encoding while preserving the 3D
characteristics of the scene. The 3D planes open up the possibility to code
multiview images with a unique representation.Postprint (author's final draft
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