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Fischer Projections

Fischer Projections

Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.

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Fused Projection-Based Point Cloud Segmentation.

Maximilian Kellner^1,2, Bastian Stahl¹, Alexander Reiterer^1,2

¹Fraunhofer Institute for Physical Measurement Techniques IPM, 79110 Freiburg, Germany.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

This summary is machine-generated.

This study introduces a 3D point cloud semantic segmentation method using 2D image projection. Fusing bird's-eye and spherical views improves accuracy and compensates for information loss in autonomous driving applications.

Keywords:

point cloud projection point cloud segmentation supervised learning

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Semantic segmentation enables computers to interpret environments by partitioning images.
Current 2D Convolutional Neural Network methods are effective but 3D approaches face computational challenges.
3D semantic segmentation is crucial for autonomous systems but requires efficient processing of point cloud data.

Purpose of the Study:

To develop an efficient 3D point cloud semantic segmentation method.
To investigate and compare different 2D projection techniques for 3D data.
To enhance segmentation accuracy by fusing complementary projection views.

Main Methods:

Projecting 3D point cloud data into 2D images for accelerated processing.
Investigating bird's-eye and spherical projection views for semantic segmentation.
Fusing multiple projection views (bird's-eye and spherical) to mitigate projection errors and information loss.
Utilizing SemanticKITTI, ParisLille, and Carla-generated synthetic data for training and evaluation.

Main Results:

Projection-based methods offer accelerated semantic segmentation of 3D point clouds.
Fusion of bird's-eye and spherical projections yields complementary benefits and improved results.
The approach demonstrates competitive performance on real-world and synthetic datasets.
Identified limitations include sensor dependency and setup inflexibility.

Conclusions:

Projecting 3D point clouds to 2D images is a viable strategy for efficient semantic segmentation.
View fusion, particularly bird's-eye and spherical, enhances robustness and accuracy.
While effective, the proposed method's flexibility is constrained by sensor specifics and deployment context.