7,914 research outputs found
Perspective-aware Convolution for Monocular 3D Object Detection
Monocular 3D object detection is a crucial and challenging task for
autonomous driving vehicle, while it uses only a single camera image to infer
3D objects in the scene. To address the difficulty of predicting depth using
only pictorial clue, we propose a novel perspective-aware convolutional layer
that captures long-range dependencies in images. By enforcing convolutional
kernels to extract features along the depth axis of every image pixel, we
incorporates perspective information into network architecture. We integrate
our perspective-aware convolutional layer into a 3D object detector and
demonstrate improved performance on the KITTI3D dataset, achieving a 23.9\%
average precision in the easy benchmark. These results underscore the
importance of modeling scene clues for accurate depth inference and highlight
the benefits of incorporating scene structure in network design. Our
perspective-aware convolutional layer has the potential to enhance object
detection accuracy by providing more precise and context-aware feature
extraction
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
Image segmentation and depth estimation are crucial tasks in computer vision,
especially in autonomous driving scenarios. Although these tasks are typically
addressed separately, we propose an innovative approach to combine them in our
novel deep learning network, Panoptic-DepthLab. By incorporating an additional
depth estimation branch into the segmentation network, it can predict the depth
of each instance segment. Evaluating on Cityscape dataset, we demonstrate the
effectiveness of our method in achieving high-quality segmentation results with
depth and visualize it with a color map. Our proposed method demonstrates a new
possibility of combining different tasks and networks to generate a more
comprehensive image recognition result to facilitate the safety of autonomous
driving vehicles
Quantum heat diode versus light emission in circuit quantum electrodynamical system
Precisely controlling heat transfer in a quantum mechanical system is
particularly significant for designing quantum thermodynamical devices. With
the technology of experiment advances, circuit quantum electrodynamics (circuit
QED) has become a promising system due to controllable light matter
interactions as well as flexible coupling strengths. In this paper, we design a
thermal diode in terms of the two-photon Rabi model of the circuit QED system.
We find that the thermal diode can not only be realized in the resonant
coupling but also achieve better performance, especially for the detuned
qubit-photon ultrastrong coupling. We also study the photonic detection rates
and their nonreciprocity, which indicates similar behaviors with the
nonreciprocal heat transport. This provides the potential to understand thermal
diode behavior from the quantum optical perspective and could shed new insight
into the relevant research on thermodynamical devices.Comment: 12 pages, 12 figures. To appear in Physical Review
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201
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