**Disp R-CNN** is a 3D object detection system for stereo images. It utilizes an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, a statistical shape model is used to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds.

CondInst is a simple yet effective instance segmentation framework. It eliminates ROI cropping and feature alignment with the instance-aware mask heads. As a result, CondInst can solve instance segmentation with fully convolutional networks. CondInst is able to produce high-resolution instance masks without longer computational time. Extensive experiments show that CondInst can achieve even better performance and inference speed than [Mask R-CNN](https://paperswithcode.com/method/mask-r-cnn). It can be a strong alternative to previous ROI-based instance segmentation methods. Code is at https://github.com/aim-uofa/AdelaiDet.

CondInst

Conditional Convolutions for Instance Segmentation

Disp R-CNN

Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

**Meta Pseudo Labels** is a semi-supervised learning method that uses a teacher network to generate pseudo labels on unlabeled data to teach a student network. The teacher receives feedback from the student to inform the teacher to generate better pseudo labels. This feedback signal is used as a reward to train the teacher throughout the course of the student’s learning.

Source	Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com