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> I'd bet that if you did that on these examples you'd see that the hair, rather than being attached to the animal, is floating halfway between the animal and t

> I'd bet that if you did that on these examples you'd see that the hair, rather than being attached to the animal, is floating halfway between the animal and the background. You're correct about that, but for something like matte/depth-treshold that's exactly what you want to get a smooth and controllable transition within the limited amount of resolution you have. For that use case, especially with the fuzzy hair, it's pretty good.

modeless 2y ago
It's not exactly what you want because you will get both background bleeding into the foreground and clipping of the parts of the foreground that fall under your threshold. What you want is for the neural net to estimate the different color contributions of background and foreground at each pixel so you can separate them without bleeding or clipping.
- incrudible 2y ago
  It's what you'd want out of a depth map used for that purpose. What you're describing is not a depth map.
  - zardo 2y ago
    Maybe the depthMap should only accept images that have been typed as hairless.
- Stedag 2y ago
  I work on Time-of-flight camera's that need to handle the kind of data that you're referring too. Each pixel takes a multiple measurements over time of the intensity of reflected light that matches the emission pulse encodings. The result is essentially a vector of intensity over a set of distances. A low depth resolution example of reflected intensity by time (distance): i: _ _ ^ _ ^ - _ _ d: 0 1 2 3 4 5 6 7 In the above example, the pixel would exhibit an ambiguity between distances of 2 and 4. The simplest solution is to select the weighted average or median distance, which results in "flying pixels" or "mixed pixels" for which there are existing efficient techniques for filtration. The bottom line is that for applications like low-latency obstacle detection on a cost-constrained mobile robot, there's some compression of depth information required. For the sake of inferring a highly realistic model from an image, Neural radiance fields or gaussian splats may best generate the representation that you might be envisioning, where there would be a volumetric representation of material properties like hair. This comes with higher compute costs however and doesn't factor in semantic interpretation of a scene. The Top performing results in photogrammetry have tended to use a combination of less expensive techniques like this one to better handle sparsity of scene coverage, and then refining the a result using more expensive techniques [1]. 1: https://arxiv.org/pdf/2404.08252 https://arxiv.org/pdf/2404.08252

modeless 2y ago

It's not exactly what you want because you will get both background bleeding into the foreground and clipping of the parts of the foreground that fall under your threshold. What you want is for the neural net to estimate the different color contributions of background and foreground at each pixel so you can separate them without bleeding or clipping.

incrudible 2y ago
It's what you'd want out of a depth map used for that purpose. What you're describing is not a depth map.
- zardo 2y ago
  Maybe the depthMap should only accept images that have been typed as hairless.
Stedag 2y ago
I work on Time-of-flight camera's that need to handle the kind of data that you're referring too. Each pixel takes a multiple measurements over time of the intensity of reflected light that matches the emission pulse encodings. The result is essentially a vector of intensity over a set of distances. A low depth resolution example of reflected intensity by time (distance): i: _ _ ^ _ ^ - _ _ d: 0 1 2 3 4 5 6 7 In the above example, the pixel would exhibit an ambiguity between distances of 2 and 4. The simplest solution is to select the weighted average or median distance, which results in "flying pixels" or "mixed pixels" for which there are existing efficient techniques for filtration. The bottom line is that for applications like low-latency obstacle detection on a cost-constrained mobile robot, there's some compression of depth information required. For the sake of inferring a highly realistic model from an image, Neural radiance fields or gaussian splats may best generate the representation that you might be envisioning, where there would be a volumetric representation of material properties like hair. This comes with higher compute costs however and doesn't factor in semantic interpretation of a scene. The Top performing results in photogrammetry have tended to use a combination of less expensive techniques like this one to better handle sparsity of scene coverage, and then refining the a result using more expensive techniques [1]. 1: https://arxiv.org/pdf/2404.08252 https://arxiv.org/pdf/2404.08252