3 ms·

Some of my friends recently put out http://gibsonenv.stanford.edu/ http://gibsonenv.stanford.edu/ Full simulation with realistic 3D spaces, enables embodied ag

Some of my friends recently put out http://gibsonenv.stanford.edu/ http://gibsonenv.stanford.edu/ Full simulation with realistic 3D spaces, enables embodied agents to interact and learn from real-world spaces. Not forest trails, but a real world environment. If you really want to create a 3D model of forest trails, photogrammetry should be sufficient, because forest scenes are richly-textured.

TaylorAlexander 7y ago
Yes I did come across Gibsonenv and it looks great for indoor scenes. As far as photogrammetry of forest trails, I found it to be very computationally intensive (taking a GCE 32 core instance 30+ hours using 90+GB of ram to compute a scene, only with errors that made it unusable). It felt very heavy handed and given all the great work I've seen in scene understanding using neural nets, it seems like deep learning would be a promising approach here. Maybe there is commercial photogrammetry software that has better pipelines, but I want to be able to compute my scenes on linux and use hundreds of images. I did my computation with OpenSFM and OpenMVS. Both wonderful projects for being free and open source. I did get a lot of great results. But I am convinced a simpler way is possible with deep learning.
- Fission 7y ago
  OpenSFM is quite out of date, so it's quite inefficient and rather inaccurate (e.g. exhaustive matching is O(n^2), and there are a lot of smarter ways that are closer to O(n)) Also, one of the main steps of mesh reconstruction is depth map generation. It typically takes anywhere from 30-75% of compute time for dense reconstruction, IF it's parallelized thru GPU. If you're using the CPU only to calculate depth maps, you're probably slowing yourself down by an order of magnitude. If you have a GPU, and use a better SFM-MVS solution, then you can quite easily reconstruct datasets of 1k-10k images within 24 hours.
  - jasonjs 7y ago
    What would you recommend as a better SFM-MVS solution?

TaylorAlexander 7y ago

Yes I did come across Gibsonenv and it looks great for indoor scenes. As far as photogrammetry of forest trails, I found it to be very computationally intensive (taking a GCE 32 core instance 30+ hours using 90+GB of ram to compute a scene, only with errors that made it unusable). It felt very heavy handed and given all the great work I've seen in scene understanding using neural nets, it seems like deep learning would be a promising approach here. Maybe there is commercial photogrammetry software that has better pipelines, but I want to be able to compute my scenes on linux and use hundreds of images. I did my computation with OpenSFM and OpenMVS. Both wonderful projects for being free and open source. I did get a lot of great results. But I am convinced a simpler way is possible with deep learning.

Fission 7y ago
OpenSFM is quite out of date, so it's quite inefficient and rather inaccurate (e.g. exhaustive matching is O(n^2), and there are a lot of smarter ways that are closer to O(n)) Also, one of the main steps of mesh reconstruction is depth map generation. It typically takes anywhere from 30-75% of compute time for dense reconstruction, IF it's parallelized thru GPU. If you're using the CPU only to calculate depth maps, you're probably slowing yourself down by an order of magnitude. If you have a GPU, and use a better SFM-MVS solution, then you can quite easily reconstruct datasets of 1k-10k images within 24 hours.
- jasonjs 7y ago
  What would you recommend as a better SFM-MVS solution?