3 ms·

Concur with the article's premise. I've been working in AI/ML and some CV projects over the last decade. I've seen far too many cases where the "algorithms" a

Concur with the article's premise. I've been working in AI/ML and some CV projects over the last decade. I've seen far too many cases where the "algorithms" and modeling teams had no concern or even a concept for how the input systems for data that would be used to train models, and later inference, mattered to the quality of outcomes. In CV and computational photography cases, there was little concern or understanding for how photography and imaging actually works, nor consideration for how variances in hardware, configuration, or the capture pipeline in general might affect those models when doing training data capture in parallel. Adding another layer, consideration for how variances between the capture pipeline and actual inference pipelines might need to be accounted for when designing the overall system and how to approach data collection and curation for training + evaluation, as well as to build-in robustness. (similar thoughts apply to concepts like bias and fairness in models) Example: training vision models using one set of imaging hardware and configurations while applying those models on very different imaging hardware with different characteristics. To summarize the above, not caring about calibration in CV is like not caring about how variances in feature extraction/embedding generation will affect the overall quality of your results.

jvanderbot 2y ago
I've noticed a really fun trend at the companies I've worked with. 10-15 years ago stereo vision was pretty reliable for feature tracking or dense navigation. Pose graph methods were stellar. A new grad student could make a pretty decent pipeline themselves. Then detectors became AI/CNN driven and stereo tracking became really good. Like radar good. Again, couple knowledgeable grad students, hard studiers, or PhD could do magic. But then it all crashed. Somewhere along the line we lost something. My last job (not Shield), I watched the estimates for a truck jump in and out of existence at 50m range with 2m baseline. Its orientation flickered 30degrees or more. But because they had used an EKF somewhere, they claimed it was the best they could do and anything else was inventing signal. I haven't dug into state estimation in the last five years but it's not gotten better if this is what new grads believe.
- midjji 2y ago
  Very few know the old methods. Few were trained, and deep learning started working just 5 years after classics got good and easy enough that a few people could use it for a product. So there was never the needed wave of non deep learning based computer vision students, they all went to deep learning. Many of the libraries are not well maintained. I needed a basic homography estimation recently and asked a minion to try opencv for it before anything more advanced. He got it working, but the best inlier ratio for every feature he tried with default parameters was 3%. The images were offset by 30 pixels left right, and less than a pixel in warp and taken with different exposure time. So he used sift... He argued he got it working and that was as good as keypoint matching got... If I hadnt happened to be the guy who needed it one step up, it could have just propagated, someone adding a shitty ekf to make it smooth, then buried in layers of heuristics and api.
  - tomp 2y ago
    Hey, I’m very interested in learning more about sfm, 3d reconstruction, slam etc. primarily for robotics applications (3D vision). Currently still a hobby project but I’m more than happy to throw money at it. If you’d be willing to share some wisdom or help otherwise, shoot me an email!
    - midjji 2y ago
      Will do
- moandcompany 2y ago
  Part of this is a corruption in the field/industry that results from greed and chasing a gold rush. Many "AI/ML" teams have been doing more alchemy than science. One memorable team "revelation" was when they figured out the hard way that many objects and human subjects, in particular, register completely differently based on the imaging spectrum used (e.g. human visible light, versus near infrared wavelengths). People familiar with the "old ways" probably would have considered this to be obvious, but it seems that the "new ways" are often correlated with an absence of domain knowledge related to what the "new ways" are being applied to. This isn't a new story, and history seems to repeat itself.

jvanderbot 2y ago

I've noticed a really fun trend at the companies I've worked with. 10-15 years ago stereo vision was pretty reliable for feature tracking or dense navigation. Pose graph methods were stellar. A new grad student could make a pretty decent pipeline themselves. Then detectors became AI/CNN driven and stereo tracking became really good. Like radar good. Again, couple knowledgeable grad students, hard studiers, or PhD could do magic. But then it all crashed. Somewhere along the line we lost something. My last job (not Shield), I watched the estimates for a truck jump in and out of existence at 50m range with 2m baseline. Its orientation flickered 30degrees or more. But because they had used an EKF somewhere, they claimed it was the best they could do and anything else was inventing signal. I haven't dug into state estimation in the last five years but it's not gotten better if this is what new grads believe.

midjji 2y ago
Very few know the old methods. Few were trained, and deep learning started working just 5 years after classics got good and easy enough that a few people could use it for a product. So there was never the needed wave of non deep learning based computer vision students, they all went to deep learning. Many of the libraries are not well maintained. I needed a basic homography estimation recently and asked a minion to try opencv for it before anything more advanced. He got it working, but the best inlier ratio for every feature he tried with default parameters was 3%. The images were offset by 30 pixels left right, and less than a pixel in warp and taken with different exposure time. So he used sift... He argued he got it working and that was as good as keypoint matching got... If I hadnt happened to be the guy who needed it one step up, it could have just propagated, someone adding a shitty ekf to make it smooth, then buried in layers of heuristics and api.
- tomp 2y ago
  Hey, I’m very interested in learning more about sfm, 3d reconstruction, slam etc. primarily for robotics applications (3D vision). Currently still a hobby project but I’m more than happy to throw money at it. If you’d be willing to share some wisdom or help otherwise, shoot me an email!
  - midjji 2y ago
    Will do
moandcompany 2y ago
Part of this is a corruption in the field/industry that results from greed and chasing a gold rush. Many "AI/ML" teams have been doing more alchemy than science. One memorable team "revelation" was when they figured out the hard way that many objects and human subjects, in particular, register completely differently based on the imaging spectrum used (e.g. human visible light, versus near infrared wavelengths). People familiar with the "old ways" probably would have considered this to be obvious, but it seems that the "new ways" are often correlated with an absence of domain knowledge related to what the "new ways" are being applied to. This isn't a new story, and history seems to repeat itself.