**DROID-SLAM** is a deep learning based SLAM system. It consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. This layer leverages geometric constraints, improves accuracy and robustness, and enables a monocular system to handle stereo or RGB-D input without retraining. It builds a dense 3D map of the environment while simultaneously localizing the camera within the map.

**PipeMare** is an asynchronous (bubble-free) pipeline parallel method for training large neural networks. It involves two main techniques: learning rate rescheduling and discrepancy correction.

PipeMare

PipeMare: Asynchronous Pipeline Parallel DNN Training

DROID-SLAM

DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras

**ZoomNet** is a 2D human whole-body pose estimation technique. It aims to localize dense landmarks on the entire human body including face, hands, body, and feet. ZoomNet follows the top-down paradigm. Given a human bounding box of each person, ZoomNet first localizes the easy-to-detect body keypoints and estimates the rough position of hands and face. Then it zooms in to focus on the hand/face areas and predicts keypoints using features with higher resolution for accurate localization. Unlike previous approaches which usually assemble multiple networks, ZoomNet has a single network that is end-to-end trainable. It unifies five network heads including the human body pose estimator, hand and face detectors, and hand and face pose estimators into a single network with shared low-level features.

Source	DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com