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