Realistic visualization of solar system small bodies using Blender ray tracing software

Penttilä, Antti; Palos, Mario F.; Kohout, T.

doi:10.5194/epsc2021-791

Cited by 2 publications

(2 citation statements)

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“…It is worth mentioning that the camera model in Blender works differently from a real CMOS sensor. This gap can be mitigated, as shown in [ 34 ]. However, this method requires real measurements or very accurate lab data to create a realistic photometric model [ 35 ], which the authors do not have access to at this time.…”

Section: Methodsmentioning

confidence: 99%

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Ghilardi,

Furfaro

2023

Sensors

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Hazard detection is fundamental for a safe lunar landing. State-of-the-art autonomous lunar hazard detection relies on 2D image-based and 3D Lidar systems. The lunar south pole is challenging for vision-based methods. The low sun inclination and the terrain rich in topographic features create large areas in shadow, hiding the terrain features. The proposed method utilizes a vision transformer (ViT) model, which is a deep learning architecture based on the transformer blocks used in natural language processing, to solve this problem. Our goal is to train the ViT model to extract terrain features information from low-light RGB images. The results show good performances, especially at high altitudes, beating the UNet, one of the most popular convolutional neural networks, in every scenario.

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Section: Methodsmentioning

confidence: 99%

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Ghilardi,

Furfaro

2023

Sensors

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“…This prevents direct modeling of spectral phenomena. To render a spectral image, we must render each wavelength separately, as a gray-scale image as suggested in (Penttilä et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Hyperblend: Simulating Spectral Reflectance and Transmittance of Leaf Tissue With Blender

Riihiaho

Rossi

Pölönen

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Remotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.

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Realistic visualization of solar system small bodies using Blender ray tracing software

Cited by 2 publications

References 0 publications

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Hyperblend: Simulating Spectral Reflectance and Transmittance of Leaf Tissue With Blender

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