Frustum PointNets for 3D Object Detection From RGB-D Data

Frustum PointNets for 3D Object Detection from RGB-D Data Charles R. Qi1∗ Wei Liu2 Chenxia Wu2 Hao Su3 Leonidas J. Guibas1 1Stanford University 2Nuro, Inc. 3UC San Diego Abstract In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

What Have We Learned From Deep Representations for …

what these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features

  What, Have, Learned, What we have learned, Have learned

Finding Tiny Faces in the Wild With Generative Adversarial ...

faces, which are unfriendly for the face classifier. Toward-s this end, we design a refinement sub-network to recover some detailed information. In the discriminator network, the basic GAN [17, 12, 8] is trained to distinguish the real and fake high resolution images. To classify faces or non-

  Faces

Squeeze-and-Excitation Networks - openaccess.thecvf.com

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Network, Excitation, Squeeze and excitation networks, Squeeze

RegularFace: Deep Face Recognition via Exclusive ...

RegularFace: Deep Face Recognition via Exclusive Regularization Kai Zhao Jingyi Xu Ming-Ming Cheng ∗ TKLNDST, CS, Nankai University kaiz.xyz@gmail.com cmm@nankai.edu.cn

  Zhao

Protecting World Leaders Against Deep Fakes

Protecting World Leaders Against Deep Fakes Shruti Agarwal and Hany Farid University of California, Berkeley Berkeley CA, USA {shrutiagarwal, hfarid}@berkeley.edu

Auto-DeepLab: Hierarchical Neural Architecture Search for ...

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Chenxi Liu1∗, Liang-Chieh Chen 2, Florian Schroff2, Hartwig Adam2, Wei Hua2, Alan Yuille1, Li Fei-Fei3 1Johns Hopkins University 2Google 3Stanford University Abstract Recently, NeuralArchitectureSearch(NAS)hassuccess-

PointNet: Deep Learning on Point Sets ... - CVF Open Access

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University

  Open, Learning, Points, Deep, Sets, Pointnet, Deep learning on point sets

Class-Balanced Loss Based on Effective Number of Samples

and large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes

  Scale, Visual, Recognition, Visual recognition

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

ESRGAN: EnhancedSuper-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Yu Qiao , and Chen Change Loy5 1 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 The Chinese University of Hong Kong, …

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks

Unsupervised Learning of Probably Symmetric Deformable 3D ...

Single views only 3D reconstruction Textured Re-lighting Figure 1: Unsupervised learning of 3D deformable objects from in-the-wild images. Left: Training uses only single views of the object category with no additional supervision at all (i.e. no ground-truth 3D information, multiple views, or any prior model of the object).

  Multiple, Image, Reconstruction, 3d reconstruction

Radiology Coding - AHIMA

Acquisition of CT data and reconstruction of PET data in multiple planes Using a computer workstation, the physician overlays PET and CT images to create images for anatomic correlation Physician reviews 3 sets of images • PET Scans • CT anatomical localization data • Combined - images superimposed Images are interpreted by physician and

  Multiple, Image, Reconstruction, Radiology, Ahima

Automated Segmentation and Morphometry of Cell and …

estimation of the 3D point spread function from images and simulation of microscopic image formation. Image-Pro Plus is a package specialized in 2D and 3D image processing, enhancement, and analysis developed by Media Cybernetics, USA. Among its advanced capabilities can be counted image fusion, extended depth of view, object tracking ...

  Image, Segmentation

ffirstname.lastnameg@tue.mpg.de arXiv:2011.12100v2 [cs.CV ...

3D geometry has gained popularity in learning-based 3D reconstruction [11,12,22,59,60,65,67,69,76] and has been extended to scene-level reconstruction [8,13,35,72, 79]. To overcome the need of 3D supervision, several works [50,51,66,81,92] propose differentiable rendering techniques. Mildenhall et al. [61] propose Neural Radiance

  Reconstruction, 3d reconstruction

Epipolar Geometry and the Fundamental Matrix

The camera centres, 3-space pointX, and its images x and x′ lie in a common plane π. (b) An image point x back-projects to a ray in 3-space defined by the first camera centre, C, and x. This ray is imaged as a line l′ in the second view. The 3-space pointXwhich projects to xmust lie on this ray, so the image of Xin the second view must ...

  Image, Geometry, Epipolar, Epipolar geometry