Understanding Indirect Time-of-Flight Depth Sensing
devblogs.microsoft.com
Understanding Indirect Time-of-Flight Depth Sensing
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Re: Understanding Indirect Time-of-Flight Depth Sensing
#2The article doesn't mention the main drawback of ToF depth sensing: Multipath Errors. They originate from light bouncing around in the scene before coming back to the detector, causing the resulting depth maps to have dents and distortions in the neighborhood of angled surfaces.
They are a big problem in built environments which have lots of 90 deg angles that act as retroreflectors to the signal. To my knowledge none of the ToF sensor manufacturers (MS, Sony, PMD, Samsung, etc..) has solved this problem. If anyone has, please let me know as the topic is of professional interest to me.
Re: Understanding Indirect Time-of-Flight Depth Sensing
#3The article doesn't mention the main drawback of ToF depth sensing: Multipath Errors. They originate from light bouncing around in the scene before coming back to the detector, causing the resulting depth maps to have dents and distortions in the neighborhood of angled surfaces. They are a big problem in built environments which have lots of 90 deg angles that act as retroreflectors to the signal. To my knowledge non…
If you know that the surfaces in your data should be flat and the corners sharp, it may be possible to filter out the fly pixels pretty well in a post-processing step. Of course, if you can't make these assumptions, then the problem is ill-posed at post-processing time.
Presumably my cursory experience with this from half a decade ago is not news to you if you have professional interest in the topic, but maybe you can elaborate how this is not a feasible solution in your case?
Re: Understanding Indirect Time-of-Flight Depth Sensing
#4Do LIDARs work under the same phase shift based ToF sensing?
Re: Understanding Indirect Time-of-Flight Depth Sensing
#5The article doesn't mention the main drawback of ToF depth sensing: Multipath Errors. They originate from light bouncing around in the scene before coming back to the detector, causing the resulting depth maps to have dents and distortions in the neighborhood of angled surfaces. They are a big problem in built environments which have lots of 90 deg angles that act as retroreflectors to the signal. To my knowledge non…
The article is also a bit optimistic in regards to outdoors use, with direct sunlight exposure. The sensors I tested in the past just didn't work at all.
Re: Understanding Indirect Time-of-Flight Depth Sensing
#6How do these ToF systems deal with multiple sensors pointing at the same scene? I've seen it work with two ToF sensors, but haven't been able to find a good explanation for how it works.
Re: Understanding Indirect Time-of-Flight Depth Sensing
#7How do these ToF systems deal with multiple sensors pointing at the same scene? I've seen it work with two ToF sensors, but haven't been able to find a good explanation for how it works.
Can solve this using polarized light. If course the sensors have to be calibrated relative to each other.
Re: Understanding Indirect Time-of-Flight Depth Sensing
#8How do these ToF systems deal with multiple sensors pointing at the same scene? I've seen it work with two ToF sensors, but haven't been able to find a good explanation for how it works.
Time division multiplex the ToF signal.
Azure Kinect uses a 3.5mm jack to sync this between sensors.
Re: Understanding Indirect Time-of-Flight Depth Sensing
#9Do LIDARs work under the same phase shift based ToF sensing?
LIDARs are a ToF sensing system but use a rotating sensor or some other motion to capture many points at a time over a field of view. Imagine taking the sensor in the Microsoft article and having several of them on something that scans back and forth across a field of view.
Some LIDARs do use phase shift as described. Others can use a pulse of light and measure that directly, while others use frequency modulation. Phase shift is just one way to measure distance using light.
Re: Understanding Indirect Time-of-Flight Depth Sensing
#10I strongly feel that for computer vision to take the next step, it needs to work on a predictive basis, using directed sampling and disentangled semantic priors, etc.
That would mean that before percepts are updated and available for applications (with knowledge like depth), a lot of information, not just the current frame, has already been integrated. Information such as previous frames, common shapes or surfaces, objects, the current full scene model, etc. That will make the system significantly more efficient and robust. And also enable things like true video understanding including the 3d structure of scenes.
Definitely easier said than done of course.