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Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

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Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#41

Post by jasonjs »

Fission wrote: post #36
TaylorAlexander wrote: post #30

Earlier quoted context omitted.

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 wou…

OpenSFM is quite out of date, so it's quite inefficient and rather inaccurate (e.g. exhaustive matching is O(n^2), and there are a lot of smarter ways that are closer to O(n)) Also, one of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, y…

What would you recommend as a better SFM-MVS solution?

Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#42

Post by enriquto »

joshvm wrote: post #23

Always worth looking at a point cloud versus a disparity map. Grayscale disparity/depth maps are somewhat misleading - the large regions of constant intensity suggest that the algorithm is good at segmenting areas of constant depth. However, the flickering in the map suggests that if you actually tried to plot this in 3D, it'd be pretty noisy. Not to disparage the result, but 2D depth/disparity maps tend to look bett…

to visualize depth maps it is best to look at derivatives of them (e.g., a directional derivative or the laplacian). Mapping the depths to indensities directly loses a lot of information.

Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#44

Post by okusername »

p1necone wrote: post #35

At this point I feel like I'm psychic. Every single time I see an image processing project posted on here I think to myself "I bet the only examples are tiny low resolution thumbnails" and every . single . time . I'm proven right. Whyyyyyyy? To be fair, this particular application doesn't really need more to show it's improvement over other approaches, but still.

Because it's a lot less data to crunch for the network.

Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#45

Post by [deleted] »

TaylorAlexander wrote: post #18
Fission wrote: post #13

Earlier quoted context omitted.

I personally do not believe that depth generated purely from deep learning can be used as input to photogrammetry anytime soon. Photogrammetry works exceedingly well because the depth maps that they generate are quite precise and accurate, and mesh reconstruction usually assumes that these points are quite close to ground truth. Deep learning approaches usually have medium accuracy but low precision, which causes the…

Interesting. Perhaps my idea of this being inserted in to existing algorithms would not work. However I do ultimately seek a low accuracy “visually approximate” 3D scene that I could use for simulation purposes. I guess I could rephrase my desire as: I’d love to see this kind of approach used to train an end to end deep learning photogrammetry system. I feel like the parallel nature of neural nets as well as their ab…

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