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Stereo matching using CUDA
Dense stereo matching is crucial for creating depth maps. However, it is very computationally expensive on CPUs. As a result the update rate is low, which makes it unusable for many tasks, eg. avoidance in dynamic environments.
Especially on drones the computation power is limited. Through the introduction of the Nvidia Jetson series computers we got access to lightweight embedded GPUs. Running the stereo matching on a GPU can potentially make it significantly faster.
Dense stereo matching is crucial for creating depth maps. However, it is very computationally expensive on CPUs. As a result the update rate is low, which makes it unusable for many tasks, eg. avoidance in dynamic environments.
Especially on drones the computation power is limited. Through the introduction of the Nvidia Jetson series computers we got access to lightweight embedded GPUs. Running the stereo matching on a GPU can potentially make it significantly faster.
Dense stereo matching is crucial for creating depth maps. However, it is very computationally expensive on CPUs. As a result the update rate is low, which makes it unusable for many tasks, eg. avoidance in dynamic environments. Especially on drones the computation power is limited. Through the introduction of the Nvidia Jetson series computers we got access to lightweight embedded GPUs. Running the stereo matching on a GPU can potentially make it significantly faster.
Create a dense stereo matching pipeline utilizing the CUDA capabilities of Nvidia Jetson devices.
Create a dense stereo matching pipeline utilizing the CUDA capabilities of Nvidia Jetson devices.
- Kevin Kleber: kevinkleber@ifi.uzh.ch
- Kunal Shrivastava: shrivastava@ifi.uzh.ch
- Philipp Foehn: foehn@ifi.uzh.ch
- Kevin Kleber: kevinkleber@ifi.uzh.ch - Kunal Shrivastava: shrivastava@ifi.uzh.ch - Philipp Foehn: foehn@ifi.uzh.ch