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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Asymmetric Encoder-Decoder Structured FCN Based LiDAR to Color Image Generation.

Hyun-Koo Kim¹, Kook-Yeol Yoo¹, Ju H Park²

¹Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38544, Korea.

Sensors (Basel, Switzerland)

|November 8, 2019

Summary

This study introduces a novel method to generate color images from LiDAR 3D reflection intensity using a fully convolutional network (FCN). The FCN produces high-quality, shadow-free images, outperforming existing methods.

Keywords:

LiDAR imaging LiDAR sensor advanced driver assistance system asymmetric network model image generation

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Area of Science:

Computer Vision
Robotics
Sensor Fusion

Background:

Light Detection and Ranging (LiDAR) provides 3D structural information but lacks color.
Generating color images from sparse LiDAR data is challenging.
Existing methods like interpolation and Generative Adversarial Networks (GANs) have limitations.

Purpose of the Study:

To propose a novel method for generating color images from LiDAR 3D reflection intensity.
To develop a Fully Convolutional Network (FCN) capable of handling sparse input data.
To evaluate the performance of the proposed method against conventional techniques.

Main Methods:

A two-step approach: projecting LiDAR 3D reflection intensity to 2D, then using an FCN for color image generation.
Designing an FCN with an asymmetric structure (deeper decoder than encoder) to manage sparse data.
Training and evaluating the FCN on the KITTI dataset.

Main Results:

The proposed method generates color images with good visual quality and accurate color fidelity.
The FCN demonstrates superior performance compared to interpolation methods and Pix2Pix.
Generated images are notably shadow-free and appear as daylight images.

Conclusions:

The asymmetric FCN effectively generates color images from sparse LiDAR intensity data.
The method offers a significant improvement over existing techniques for LiDAR-based colorization.
The inherent properties of LiDAR data enable the generation of shadow-free, consistent color images.