NVOFA Application Note

NVOFA Application Note (PDF)

NVIDIA® GPUs, starting with the NVIDIA Turing™ generation, contain a hardware accelerator for computing optical flow and stereo disparity between frames (referred to as NVOFA in this document), which works independently of graphics/NVIDIA CUDA® cores. With end-to-end optical flow calculation offloaded to NVOFA, the graphics/CUDA cores and the CPU are free for other operations.

Optical flow vectors are useful in various use-cases such as object detection and tracking, video frame rate up-conversion, depth estimation, stitching etc. It is also observed that using flow vectors for object detection also increases inference accuracy1.

The hardware capabilities of NVOFA are exposed through APIs referred to as NVOF APIs.


NVOFA Capabilities

NVOFA engine can operate in two modes:

  • Optical Flow Mode: In this mode, the engine generates flow vectors between two given frames, returning both X and Y components of the flow vectors. The Vulkan interface does not support Optical Flow mode for GPUs based on the Turing architecture.
  • Stereo Disparity Mode: In this mode, the engine generates flow vectors in X direction only. This mode is useful in use-cases in which the Y-component of the vectors is not required, or it is known a priori that it is zero (e.g. finding disparity between the left and right images of a stereo capture). Stereo Disparity mode will be deprecated in future SDK releases. Client applications can alternatively use the X component of the flow generated in Optical Flow mode. The Vulkan interface does not support Stereo Disparity mode.

The hardware generates flow vectors block-wise, one vector for each block of 4 × 4, 2 x 2 and 1 x 1 pixels (referred to as grid).   The generated vectors can be further post-processed in software to improve accuracy; up sampled to produce dense flow map.

NVOFA hardware natively supports multiple hardware contexts with negligible context-switching penalty. As a result, subject to the hardware performance limit and available memory, an application can generate motion vectors for multiple contexts simultaneously.

The NVOFA hardware is supported for all Turing GPUs (except TU117) and above.


Capabilities of NVOFA are exposed via NVOF APIs. NVOF API includes three types of software interfaces:

  • CUDA: Cross-platform API, works on Linux and Windows 10 and above.
  • DirectX 11: Works on Windows 10 and above.
  • DirectX 12: Works on Windows 10 20H1 and above.
  • Vulkan: Cross-platform API, works on Linux and Windows 10 and above. Vulkan interface is not supported on WSL(Window subsystem for Linux) architecture.

Refer to the sample applications included in the Optical Flow SDK for more details.

Table 1 and Table 2 summarize the capabilities of the NVOFA hardware and the new features exposed through NVOF APIs in Optical Flow SDK 5.x respectively.

Table 1. NVOFA Hardware Capabilities
Hardware Features Turing Ampere Ada
Optical flow mode Y Y Y
Support for external hints Y Y Y
4x4 grid size Y Y Y
2x2 and 1x1 grid size N Y Y
Hardware cost N Y Y
Region of interest (ROI) optical flow calculation N Y Y
Maximum supported resolution 4096x4096 8192x8192 8192x8192

  • Y: Supported, N: Unsupported

Table 2. New features exposed through NVOF SDK 5.x
Sr. No. SDK Version Feature Description
1 5.0 Vulkan interface This SDK adds support of Vulkan interface. Application can generate the flow for a pair of Vulkan resources.

NVOFA Quality and Performance

The NVOF API exposes multiple quality and performance levels (which are referred to as presets) which the user can choose based on the desired quality and performance requirement. Figure 1 shows the performance/quality trade-off to be expected with the presets.

Figure 1. Performance/Quality Presets exposed in NVOF API


The NVOFA provides real time performance with small CUDA core utilization. Table 3 shows indicative2 performance in FPS with a 1080p video sequence and quality of NVOF API on KITTI 2105 which is publicly available data set. Users can trade quality vs. performance by choosing the right preset. Note that performance numbers in Table 3 are measured with assumptions listed under the table. The performance varies across GPU classes (e.g. Quadro, Tesla), and scales (almost) linearly with the clock speeds for each hardware.

Table 3. Indicative quality and performance
Grid size Preset   Fl-fg Fl-all FPS at 1080p
Turing Ampere Ada Turing Ampere Ada Turing Ampere Ada
4x4 SLOW NOC 24.94 26.77 23.56 21.37 18.65 17.26 225 200 536
OCC 27.63 29.32 26.31 31.53 29.17 27.73
MEDIUM NOC 36.60 32.78 23.66 23.73 22.23 19.11 468 405 1000
OCC 38.90 35.10 26.36 33.61 32.13 28.98
FAST NOC 47.50 38.40 29.90 26.39 25.44 23.48 768 613 1296
OCC 49.42 40.65 32.44 35.91 34.85 33.07
2x2 SLOW NOC N/A 26.90 20.44 N/A 18.49 15.98 N/A 94 210
OCC 29.43 23.18 29.08 26.00
MEDIUM NOC 33.16 21.38 20.51 16.13 154 336
OCC 35.44 24.08 30.73 26.01
FAST NOC 35.44 30.81 23.09 23.13 261 711
OCC 37.75 33.28 32.92 32.41
1x1 SLOW NOC 26.91 22.71 18.72 16.14 29 98
OCC 29.45 25.44 29.16 26.42
MEDIUM NOC 30.38 26.29 20.24 16.27 45 113
OCC 32.77 28.94 30.39 26.18
FAST NOC 34.55 29.88 22.65 20.93 85 222
OCC 36.84 32.40 32.58 30.59

  • The above data is generated for Optical Flow mode using .\Samples\AppOFCuda on RTX6000, RTX3090 and RTX4090 respectively on Windows 11.
  • The performance on Windows using CUDA interface with hardware scheduling disabled is typically lesser than that of Linux and Windows with hardware scheduling enabled due to a known bug inside NVIDIA display driver.
  • All measurements are done by setting the video clocks as reported by nvidia-smi at 1679, 1708 and 2114MHz on RTX6000, RTX3090 and RTX4090 respectively. The performance should scale according to the actual video clocks for other GPUs. Information on nvidia-smi can be found at https://developer.nvidia.com/nvidia-system-management-interface.

  • Resolution/Input format: 1920x1080/YUV 4:2:0
  • Software: Optical flow SDK 5.0, NVIDIA display driver: 528.24 for Window OS and 525.85.05 for Linux
  • Fl-fg = Percentage of vectors in foreground pixels having an average EPE > 3 on KITTI 2015.
  • Fl-all = Percentage of vectors having an average EPE > 3 on KITTI 2015.
  • Details for KITTI 2015 and the test data set can be found http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow.
  • NOC = Non-occluded region
  • OCC = Occluded region

NVOFFRUC Performance

NVOFFRUC performance is measured in terms of an average time taken by NVOFFRUC library for interpolating one frame in an input sequence. It is calculated as total time taken by NVOFFRUC library to double the frame rate of input sequence divided by total number of frames in input sequence. Table 4 shows indicative3 performance as measured on publicly available dataset.

Table 4. Indicative performance
GPU Name Performance
GeForce RTX 2080 Ti 12.01 ms
GeForce RTX 3090 Ti 9.23 ms
GeForce RTX 4090 4.41 ms

  • The above data is generated with \NvOFFRUC\NvOFFRUCSample using NV12 CUDA array as input surface on Windows.
  • All measurements are done by setting the video clocks as reported by nvidia-smi at 1755 MHz. The performance should scale according to the actual video clocks for other GPUs. Information on nvidia-smi can be found at https://developer.nvidia.com/nvidia-system-management-interface.

  • Resolution/Input format: 1920x1080/YUV 4:2:0
  • Software: Optical flow SDK 4.0 and later SDKs, NVIDIA display driver: 522.25

Programming NVOFA

Optical Flow SDK 5.0 is supported on R525 drivers and above. Refer to the SDK release notes for information regarding the required driver version.

Refer to the documents and the sample applications included in the SDK package for details on how to program NVOFA.

OpenCV Support

OpenCV is one of the most popular libraries in the field of computer vision. OpenCV library contains several CPU-based and CUDA-based algorithms for computing optical flow vectors.

NVOFA can also be used with OpenCV to significantly speed up the optical flow calculation.

Note that OpenCV is an open-source project and their usage is governed by specific licenses and terms and conditions.


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Refer http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Making_Convolutional_Networks_CVPR_2018_paper.pdf and http://openaccess.thecvf.com/content_cvpr_2017/papers/Carreira_Quo_Vadis_Action_CVPR_2017_paper.pdf.

NVOFA performance depends on many factors, including but not limited to: OFAPI settings, GPU clocks, GPU type, video content type, instantaneous available memory bandwidth etc.

NVOFFRUC performance depends on many factors, including but not limited to: NvOFFRUC , GPU clocks, GPU type, video content type, instantaneous available memory bandwidth etc.
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