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This is a catalogue of my paper reading notes

​ There are mainly 3 parts of my reading.

​ The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.

​ The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.

​ The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.

​ "*" means I think it's idea is brilliant.

Computer Vision

General Conclusion

3D SLAM

  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC

Semantic Segmentation

  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Machine Learning

Robotics

About

📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.

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This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
Topics
computer-visionmachine-paper-sharingpaperreading
Resources
Readme
Activity
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1 watching
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This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
Topics
computer-visionmachine-paper-sharingpaperreading
Resources
Readme
Activity
Stars
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1 watching
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This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
Topics
computer-visionmachine-paper-sharingpaperreading
Resources
Readme
Activity
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1 watching
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This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
Topics
computer-visionmachine-paper-sharingpaperreading
Resources
Readme
Activity
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1 watching
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, 'i');
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This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
Topics
computer-visionmachine-paper-sharingpaperreading
Resources
Readme
Activity
Stars
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Watchers
1 watching
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Report repository
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Readme.md
Repository files navigation
This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
Topics
computer-visionmachine-paper-sharingpaperreading
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// Universal Dark Mode - works on any site
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if (!enabled) return;
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console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
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This is a catalogue of my paper reading notes
​	There are mainly 3 parts of my reading.
​	The first part is mainly about Computer Vision where I'm concerned in most, I will try to read some classic paper intensively while do some paper reading extensively.
​	The second part of my reading is something about Machine Learning. It's an enormous topic. From that part I will read some classic and essential paper like NERF and Attention is all you need, to extend my vision and follow the routine.
​	The third part is going to be about Robotics/Dynamics/Control Theory .etc. I plan to only do some thesis study extensively in this part.
​	"*" means I think it's idea is brilliant.
Computer Vision
General Conclusion
3D SLAM
  • ICCV 2015 *PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
  • DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
  • Learning Multi-Scene Absolute Pose Regression with Transformers
  • CVPR2022(Oral) *RPMG: Projective Manifold Gradient Layer for Deep Rotation Regression
  • PoseNetV2: Geometric loss functions for camera pose regression with deep learning
  • Visual Odometry Revisited What Should Be Learnt
  • Visual Camera Re-Localizationfrom RGB and RGB-D Images Using DSAC
Semantic Segmentation
  • CVPR2022 ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
  • CVPR2022 Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Machine Learning
Robotics
About
📖 Paper reading notes in computer vision and machine learning, especially 3D SLAM, Implicit Representation and semantic segmantation. (constantly updating!!!) Everyone is welcomed to share your ideas and comments, I would greatly appreciate it if you can point out some of my mistakes or answer my questions.
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