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