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Stereoisomers

Stereoisomers

On the basis of mirror symmetry, stereoisomers of an organic molecule can be further classified into diastereomers and enantiomers. Diastereomers are stereoisomers that are not mirror images of each other. Substituted alkenes, such as the cis and trans isomers of 2-butene, are diastereomers, as these molecules exhibit different spatial orientations of their constituent atoms, are not mirror images of each other, and do not interconvert. Here, the interconversion is suppressed due to...

Stereoisomerism

Stereoisomerism

Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula.
Transition metal complexes often exist as geometric isomers, in which the same atoms are connected through the same types of bonds but with differences in their orientation in space. Coordination complexes with two different ligands in the cis and trans positions from a ligand of interest form isomers. For example, the octahedral [Co(NH3)4Cl2]+ ion has two isomers (Figure 1) In the cis...

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Related Experiment Video

Updated: Oct 4, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Rethinking Training Strategy in Stereo Matching.

Zhibo Rao, Yuchao Dai, Zhelun Shen

IEEE Transactions on Neural Networks and Learning Systems

|February 10, 2022

Summary

This summary is machine-generated.

Training strategies like pre-training and data augmentation significantly boost stereo matching network performance on single and cross datasets. This challenges the focus on network architecture alone, improving generalization and robustness, especially on smaller datasets.

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Last Updated: Oct 4, 2025

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Published on: December 4, 2013

Area of Science:

Computer Vision
Deep Learning
Machine Learning

Background:

Learning-based stereo matching methods achieve high performance but often focus on dataset-specific network designs.
The impact of training strategies on single and cross-dataset performance is frequently overlooked.
Existing research often prioritizes network architecture over training methodologies.

Purpose of the Study:

To analyze the relationship between various training strategies and performance in stereo matching.
To investigate the influence of pre-training and data augmentation on network generalization and robustness.
To challenge the conventional emphasis on network architecture in stereo matching research.

Main Methods:

Retraining representative state-of-the-art stereo matching methods (e.g., GC-Net, PSM-Net, GA-Net) with different training strategies.
Developing and training an improved Non-Local Context Attention Network (NLCA-Net v2) with a novel strategy.
Conducting quantitative experiments on single and multiple datasets, including KITTI, ETH3D, and Middlebury.

Main Results:

Pre-training and data augmentation significantly improve network performance on both single and cross datasets without specific architectural changes.
NLCA-Net v2 achieved top performance on single and multiple datasets, securing 2nd place in the ECCV Robust Vision Challenge 2020.
Pre-training and data augmentation demonstrated a substantial impact on generalization and robustness for small datasets, sometimes exceeding the effect of network structure.

Conclusions:

Training strategies, particularly pre-training and data augmentation, are critical for stereo matching performance and generalization.
The findings suggest a paradigm shift is needed, moving beyond an excessive focus on single-dataset performance and network architecture.
Future research should explore and optimize training strategies to enhance stereo matching model robustness and applicability across diverse datasets.