EE290T, Fall 2018
Advanced Topics in Signal Processing:
3D image processing and Computer Vision
Monday: 1:00 pm to 4:00 pm
531 Cory Hall
Prerequisite: Signals and Systems at the level of EE120, basic linear algebra.
- Course Announcement
Academic Dishonesty Policy
- Piazza
- Grading:
10% class participation
50% paper presentation and/or homework
40% Class project; proposals due October 15th.
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Lecturer:
Professor Avideh Zakhor
507 Cory Hall
Phone: (510) 643-6777
avz@berkeley
Office Hours:
Monday, 4:00 pm - 5:00 pm 507 Cory Hall
Course Reader
Matthew Waliman
mwaliman@berkeley
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Texts:
(a) ″Computer Vision: Algorithms and Applications″ by Richard Szeliski; Springer PDF
(b) ″Computer Vision: A Modern Approach″ by Ponce and Forsyth PDF
(c) ″ Multiple View Geometry in Computer Vision″ Hartley and Zisserman PDF Tutorial Presentation
(d) ″Deep Learning″ Ian Goodfellow, Yoshua Bengio and Aaron Courville Book
(e) ″Probabilistic Robotics″ by Sebastian Thrun, Wolfram Burgard, and Dieter Fox PDF
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- 9/30/18
Sign up for Piazza.
Welcome to EE290T!
- Aug. 27, 2018
Introduction to 3D Reconstruction and Recognition
3D Acquisition
- September 10, 2018
SLAM Slides
Reading
- September 17, 2018
Reading: Chapter 2 of "Probablistic Robotics
SLAM Overview
Baye's Filter and SLAM
Intro to Deep Learning and Convolutional Neural Networks
- September 24, 2018
Reading
Camera Models
Camera Calibration
- October 1, 2018
Single View Geometry
Projective Geometry
Reading: Camera Models, Affine Projection
Reading: Single View Geometry
- October 8, 2018
Vanishing Points and Lines
- October 15, 2018
Reading: Chapter 8 Hartley and Zimmerman
Vanishing Points and Lines
How to Compute Camera Orientation
Importance of Camera Center
- October 22, 2018
Learning Methods for Single View Geometry
Intuition Behind Epipolar Geometry
Epipolar Geometry 1
SVD
Epipolar Geometry 2
- October 29, 2018
RANSAC
Feature Matching
Epipolar Geometry 1
Stereo Systems
- November 5, 2018
Factorization approach to structure from motion
Sparse bundle adjustment for structure from motion
Perspective-n-point
- November 26, 2018
Detectors and Descriptors
The Procustes Problem
Perspective-n-point
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- Relevant CVPR 2018 Papers
- ″SurfConv: Bridging 3D and 2D Convolution for RGBD Images″, by Hang Chu, Wei-Chiu Ma, Kaustav Kundu, Raquel Urtasun and Sanja Fidler, 2018
- ″Pointwise Convolutional Neural Networks,″ by Binh-Son, Hua Minh-Khoi Tran, and Sai-Kit Yeung, 2018
- ″Recurrent Slice Networks for 3D Segmentation of Point Clouds,″ by Qiangui Huang, Weiyue Wang, and Ulrich Neumann, 2018
- ″Surface Networks,″ by Ilya Kostrikov1, Zhongshi Jiang1, Daniele Panozzo, Denis Zorin, and Joan Bruna, 2018
- ″CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition,″ by Jedrzej Kozerawski and Matthew Turk, 2018
- ″Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs,″ by Loic Landrieu and Martin Simonovsky, 2018
- ″Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net,″ by Wenjie Luo, Bin Yang and Raquel Urtasun, 2018
- ″Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras,″ by Pedro Miraldo, Francisco Eiras, and Srikumar Ramalingam, 2018
- ″3D Object Detection with Latent Support Surfaces,″ by Zhile Ren and Erik B. Sudderth, 2018
- ″A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation,″ by RiccardoRoveri,LukasRahmann,A.Cengiz Oztireli, and Markus Gross, 2018
- ″Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction,″ by Daeyun Shin, Charless C. Fowlkes, Derek Hoiem, 2018
- ″Im2Pano3D:
Extrapolating 360 ◦ Structure and Semantics Beyond the Field of View
,″ by Shuran Song, Andy Zeng, Angel X. Chang, Manolis Savva, Silvio Savarese, and Thomas Funkhouser, 2018
- ″SPLATNet: Sparse Lattice Networks for Point Cloud Processing,″ by Hang Su, Varun Jampani, Deqing Sun, and Subhransu Maji, 2018
- ″Tangent Convolutions for Dense Prediction in 3D,″ by Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, and Qian-Yi Zhou, 2018
- ″Deep Parametric Continuous Convolutional Neural Networks
,″ by Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, and Raquel Urtasun, 2018
- ″SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation,″ by Weiyue Wang, Ronald Yu, Qiangui Huang, and Ulrich Neumann, 2018
- ″Attentional ShapeContextNet for Point Cloud Recognition,″ by Saining Xie, Sainan Liu, Zeyu Chen, and Zhuowen Tu, 2018
- ″PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation,″ by Danfei Xu, Dragomir Anguelov, , 2018
- ″Automatic 3D Indoor Scene Modeling from Single Panorama,″ by Yang Yang, Shi Jin, Ruiyang Liu, Sing Bing Kang, and Jingyi Yu, 2018
- ″PIXOR: Real-time 3D Object Detection from Point Clouds,″ by Bin Yang, Wenjie Luo, and Raquel Urtasun, 2018
- ″Deep Depth Completion of a Single RGB-D Image,″ by Yinda Zhang and Thomas Funkhouser, 2018
- ″LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image,″by Chuhang Zou, Alex Colburn, Qi Shan, and Derek Hoiem, 2018
- ″Frustum PointNets for 3D Object Detection from RGB-D Data,″by Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas, 2018
Other Papers
- ″KinectFusion: Real-Time Dense Surface Mapping and Tracking∗,″ by Richard A. Newcombe, Andrew J. Davison, Shahram Izadi, Pushmeet Kohli, Otmar Hilliges, Jamie Shotton, David Molyneaux, Steve Hodges, David Kim, and Andrew Fitzgibbon, 2011
- ″Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction,″ by Matthias Niessner, Angela Dai and M. , 2014
- ″VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition,″ by Daniel Maturana and Sebastian Scherer, 2015
- ″ElasticFusion: Dense SLAM Without A Pose Graph,″ by Thomas Whelan, Stefan Leutenegger, Renato F. Salas-Moreno, Ben Glocker and Andrew J. Davison, 2015
- ″3D Semantic Parsing of Large-Scale Indoor Spaces,″ by Iro Armeni1 Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese, 2016
- ″FPNN: Field Probing Neural Networks for 3D Data,″ by Yangyan Li, Sören Pirk, Hao Su, Charles R. Qi, Leonidas J. Guibas, 2016
- ″Fine-To-Coarse Global Registration of RGB-D Scans,″ by Maciej Halber and Thomas Funkhouser, 2016
- ″SemanticFusion: Dense 3D Semantic Mapping with Convolutional
Neural Networks,″ by John McCormac, Ankur Handa, Andrew Davison, and Stefan Leutenegger,2016
- ″Semantic Scene Completion from a Single Depth Image,″ by Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, and Thomas Funkhouser, 2016
- ″DA-RNN: Semantic Mapping with Data Associated
Recurrent Neural Networks,″ by Yu Xiang and Dieter Fox, 2017
- ″Matterport3D: Learning from RGB-D Data in Indoor Environments,″ by Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Nießner, Manolis Savva,Shuran Song, Andy Zeng, and Yinda Zhang, 2017
- ″3DLite: Towards Commodity 3D Scanning for Content Creation,″ by Jingwei Huang, Angela Dai, Leonidas Guibas, and Matthias Niessner, 2017
- ″BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration,″ by Angela Dai Matthias Niessner, Michael Zollhoer, Shahram Izadi,
Christian Theobalt, 2017
- ″Joint 2D-3D-Semantic Data for Indoor Scene Understanding,″ by Iro Armeni, Alexander Sax, Amir R. Zamir, and Silvio Savarese, 2017
- ″PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,″ by Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas, 2017
- ″PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
,″ by Charles R. Qi Li Yi Hao Su Leonidas J. Guibas, 2017
- ″ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes,″ by Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner, 2017
- ″Predicting Complete 3D Models of Indoor Scenes,″ by Ruiqi Guo, Chuhang Zou, and Derek Hoiem, 2017
- ″Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
,″ by Jiajun Wu, Chengkai Zhang, and Tianfan Xue, 2017
- ″Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks,″ by Yinda Zhang†, Shuran Song†, Ersin Yumer, Manolis Savva, Joon-Young Lee, Hailin Jin and Thomas Funkhouser, 2017
- ″3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions,″ by Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao and Thomas Funkhouser,2017
- ″2D-Driven 3D Object Detection in RGB-D Images,″ by Jean Lahoud, Bernard Ghanem,2017
- ″3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation,″ by Angela Dai and Matthias Niessner, 2018
- ″ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans,″ by Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jugen Sturm, Matthias Nießner, 2018
- ″Deep Depth Completion of a Single RGB-D Image,″ by Yinda Zhang and Thomas Funkhouser, 2018
- ″Fusion++: Volumetric Object-Level SLAM,″ by John McCormac, Ronald Clark, Michael Bloesch, Andrew J. Davison, and Stefan Leutenegger, 2018
- ″Dynamic Graph CNN for Learning on Point Clouds,″ by Yue Wang, Yongbin Suan, Ziewi Liu, Sanjay Sarma, 2018
Open Access page for CVPR papers from 2013 to 2018
Time-of-Flight Cameras and
Microsoft Kinect: A user perspective on technology and
applications
Time of Flight Cameras: Principles, Methods, and
Applications
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Last updated 11/28/2011 by LB
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