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Frame Rate #21
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The 2Hz in the paper only relates to the sampling rate of the training sample generation process, i.e., training samples in our dataset are 0.5s apart. We have a C++ ROS Node to run trained models in inference mode. I quickly measured its runtimes for the following configuration:
Possible areas of runtime improvement:
Please also note that we have not focused on achieving minimal runtime. Typical model optimization techniques like pruning or quantization could further decrease latency. |
Thank you. This was helpful. We will probably try to replicate this as first steps and then move to see if there are improvements. For our high speeds, a transformation to birds eye may be necessary as image stitching through homography alone seems to be unreliable. |
Would you be willing to share the C++ ROS node with me? I'd like to do some experimenting and it would speed up the process to have your working node |
Please understand that at the moment we have no capacity to publicly release the inference node. We can however give you some hints on what such an inference node would need to do:
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What is the speed of this implementation? We are mostly interested in finding a way to stitch camera images and had thoughts about transferring to birds eye view for depth information. Our purpose is to implement this is on our autonmous race car, and we will be running at high speeds so traditional homography seems to have too many inaccuracies.
Your methods pose interest to us. However, given that this is on a racecar, speed is of high importance. We are currently running our cameras anywhere from 25 to 40 Hz. In the paper, you mention 2Hz. Is this a consequence from the speed of the network?
In short, what's the latency or maximum speed of the network?
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