3D Mesh and Point Cloud Generation

I’m amazed with the achievements from eCapture Research and Development SL. Today I’ve generated point clouds and 3D mesh from series of 49 photos taken via mobile vehicle. I’ve tested two products.

The first product, I call it as product A, it requires users with prior knowledge about sensor size, camera focal length, coordinate projection, etc. Experience in handling spatial data is an advantage. Took almost 30 minutes processing time and expensive computational cost (desktop based).

On the other hand, by using eyescloud3d I’ve managed to generate the model within 15 minutes (output as shown). And most importantly, no prior background knowledge needed.

Congratulations to Guillermo, Miguel and the team for your R&D. Fascinating.

Our lab at Universiti Teknologi Malaysia (UTM) have 5 years MoU research collaboration with eCapture Research and Development SL. We have the privilege to conduct scientific research using their products. Students are eligible to utilize it, feel free to let me know if you’re interested.

The accuracy? You may read our following international publications:

1. DEVELOPMENT OF 3D CITY MODEL USING VIDEOGRAMMETRY TECHNIQUE
(https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W16/221/2019/isprs-archives-XLII-4-W16-221-2019.pdf)

2. 3D MODEL FOR INDOOR SPACES USING DEPTH SENSOR.
(https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W16/471/2019/isprs-archives-XLII-4-W16-471-2019.pdf)

3. COMPARATIVE ANALYSIS OF VARIOUS CAMERA INPUT FOR VIDEOGRAMMETRY
(https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W16/63/2019/isprs-archives-XLII-4-W16-63-2019.pdf)

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