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Model Overview

Description:

NVOF is a lightweight deep learning-based network trained for estimating motion between frames. It provides models for two motion estimation tasks:

\n
Optical Flow: Estimates motion between consecutive frames in a video sequence. Optical flow is defined as the pattern of apparent motion of image objects caused by the movement of the object or camera.
\n
\n
Stereo Matching: Computes disparity maps from stereo image pairs, which can be used for depth estimation and 3D reconstruction.
\n

Both models share similar architectural principles but are trained for their specific tasks.

\n tr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n

Figure 1. NVOF real-time optical flow estimation.

\n tr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n

Figure 2. NVOF real-time disparity estimation.

Model Architecture:

Architecture Type: Convolutional Neural Network (CNN)

Network Architecture: These models are based on the widely used coarse-to-fine residual structure. First, a pyramidal feature extraction based on convolution is performed to obtain features at different scales. Then, the cost volume layer is used to calculate feature correlations. Finally, a UNet-like network decodes the extracted features and correlations into residuals to refine the upsampled outputs from the previous scale.

Model Version(s):

Version 1.0:

The initial release version includes both optical flow estimation and stereo matching models, trained on synthetic and real-world datasets.

Training and Evaluation Datasets:

Training Datasets:

Optical Flow Model

For optical flow estimation, we utilize several public datasets including TartanAir, Spring, AutoFlow, and Kubric, along with additional synthetic data generated using UE4 to create more virtual scenarios.

Stereo Matching Model

For stereo matching, we utilize several public datasets including TartanAir, FoundationStereo, and DrivingStereo. Additionally, we leverage the CARLA simulator to generate synthetic data specifically for driving scenarios.

Evaluation Datasets:

We use MPI-Sintel and KITTI2015, which are publicly available benchmarks, as our evaluation datasets for both models.

Evaluation Results:

Optical Flow Model

AEPE computes average per-pixel end-point error between estimated optical flow vector and ground-truth optical flow vector. Fl-all computes the percentage of pixels whose optical flow error is larger than 3 pixels.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Dataset	AEPE	Fl-all
Sintel-final	3.04	9.04%
Sintel-clean	2.22	5.90%

Stereo Matching Model

D1-all computes the percentage of stereo disparity outliers in the first frame.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Dataset	AEPE	D1-all
Sintel-final	4.34	13.77%
KITTI2015	1.04	4.50%

Inference:

Both models need to be used with NVIDIA hardware and software. They can run on any NVIDIA GPU including NVIDIA Jetson devices and are optimized for use with TensorRT.

Input:

Input Type(s): Image
\nInput Format(s): Red, Green, Blue (RGB)
\nInput Parameters: 4D
\nOther Properties Related to Input:

For both models, the input consists of two RGB images with pixel values in the range of 0-255.

For the optical flow model, inputs are typically consecutive frames from a video sequence.
For the stereo matching model, inputs are stereo image pairs (rectified left and right views) captured at the same time.

Channel ordering of the input should be NCHW, where:

N = Batch Size
C = Number of channels (6, representing two RGB images)
H = Height of images (must be divisible by 64)
W = Width of images (must be divisible by 64)

Output:

Output Type(s):

Optical Flow Model: Dense optical flow map (2 channels)
Stereo Matching Model: Disparity map (1 channel)

Output Format: Float32
\nOutput Parameters: 4D
\nOther Properties Related to Output:

The optical flow model outputs a dense flow map with the same dimensions as the input images, containing two channels representing motion in the x and y directions.
The stereo matching model outputs a disparity map with the same dimensions as the input images, containing one channel representing the horizontal displacement between corresponding points in the stereo pair.

Channel ordering of the output will be NCHW, where:

N = Batch Size
C = Number of channels (2 for optical flow or 1 for disparity)
H = Height of the output (same as the input)
W = Width of the output (same as the input)

Pre-generated TensorRT Engine

We provide pre-generated TensorRT engines for both models with versionCompatible and hardwareCompatibilityLevel=ampere+ that users can use directly for inference.\nThe engines were built with --minShapes=input:1x6x256x256 --optShapes=input:1x6x512x512 --maxShapes=input:1x6x1088x1920.\nThese compatibility configurations for version/hardware/resolution will make the latency of the current engines 30% - 40% slower compared to engines optimized for a specific environment.\nIf you have requirements for latency optimization tailored to your running environment or for enhancing the quality specific to your scenario, please contact us.

In the attachments, we also provide Python demos which show:

How to load and deserialize the TensorRT engines
How to get model input/output information
How to prepare input image pairs
How to run inference and get results for both optical flow and stereo matching

Please refer to the attached Python demos for detailed implementation.

Hardware Requirements

The pre-generated TensorRT engines require:

NVIDIA GPU with Ampere+ architecture
TensorRT 10.0 or newer

Real-time Inference Latency

The inference is run on the provided models at FP16 precision. The inference performance is measured using the Python demos listed above, and it does not include the memory copy time between the device and the host. To optimize the inference performance, we have used some custom plugins in the model engines. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The results shown here are the inference performance with input resolution of 1088×1920.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Platform	NVOF (Optical Flow Model) (ms)	NVOF (Stereo Matching Model) (ms)
RTX4090	2.76	2.12
A100	4.54	3.47
Jetson Orin	21.2	18.97

License/Terms of Use:

NVIDIA Open Model License Agreement

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure these models meet requirements for the relevant industry and use case and address unforeseen product misuse.

For more detailed information on ethical considerations for these models, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

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\n \n \n \n \n \n \n

Figure 1. NVOF real-time optical flow estimation.

\n\n\n

\n \n \n \n \n \n \n

Figure 2. NVOF real-time disparity estimation.

\n\n## Model Architecture:\n\n**Architecture Type:** Convolutional Neural Network (CNN) \n\n**Network Architecture:** These models are based on the widely used coarse-to-fine residual structure. First, a pyramidal feature extraction based on convolution is performed to obtain features at different scales. Then, the cost volume layer is used to calculate feature correlations. Finally, a UNet-like network decodes the extracted features and correlations into residuals to refine the upsampled outputs from the previous scale.\n\n## Model Version(s):\n\n### Version 1.0: \nThe initial release version includes both optical flow estimation and stereo matching models, trained on synthetic and real-world datasets.\n\n## Training and Evaluation Datasets:\n\n### Training Datasets:\n#### Optical Flow Model\n\nFor optical flow estimation, we utilize several public datasets including [TartanAir](https://theairlab.org/tartanair-dataset/), [Spring](https://spring-benchmark.org/), [AutoFlow](https://autoflow-google.github.io/#data), and [Kubric](https://github.com/google-research/kubric), along with additional synthetic data generated using [UE4](https://www.unrealengine.com/en-US) to create more virtual scenarios.\n\n#### Stereo Matching Model\nFor stereo matching, we utilize several public datasets including [TartanAir](https://theairlab.org/tartanair-dataset/), [FoundationStereo](https://github.com/NVlabs/FoundationStereo), and [DrivingStereo](https://drivingstereo-dataset.github.io/). Additionally, we leverage the [CARLA simulator](https://github.com/carla-simulator/carla) to generate synthetic data specifically for driving scenarios.\n\n### Evaluation Datasets:\n\nWe use [MPI-Sintel](http://sintel.is.tue.mpg.de/) and [KITTI2015](https://www.cvlibs.net/datasets/kitti/index.php), which are publicly available benchmarks, as our evaluation datasets for both models.\n\n### Evaluation Results:\n\n#### Optical Flow Model\n\n`AEPE` computes average per-pixel end-point error between estimated optical flow vector and ground-truth optical flow vector. `Fl-all` computes the percentage of pixels whose optical flow error is larger than 3 pixels.\n\n| Dataset | AEPE | Fl-all |\n| :---: | :---: | :---: |\n| Sintel-final | 3.04 | 9.04% |\n| Sintel-clean | 2.22 | 5.90% |\n\n#### Stereo Matching Model\n`D1-all` computes the percentage of stereo disparity outliers in the first frame.\n\n| Dataset | AEPE | D1-all |\n| :---: | :---: | :---: |\n| Sintel-final | 4.34 | 13.77% |\n| KITTI2015 | 1.04 | 4.50% |\n\n## Inference:\nBoth models need to be used with NVIDIA hardware and software. They can run on any NVIDIA GPU including NVIDIA Jetson devices and are optimized for use with TensorRT.\n\n### Input:\n\n**Input Type(s):** Image \n**Input Format(s):** Red, Green, Blue (RGB) \n**Input Parameters:** 4D \n**Other Properties Related to Input:** \n\nFor both models, the input consists of two RGB images with pixel values in the range of 0-255. \n\n- For the optical flow model, inputs are typically consecutive frames from a video sequence.\n- For the stereo matching model, inputs are stereo image pairs (rectified left and right views) captured at the same time.\n\nChannel ordering of the input should be NCHW, where:\n- N = Batch Size\n- C = Number of channels (6, representing two RGB images)\n- H = Height of images (must be divisible by 64)\n- W = Width of images (must be divisible by 64)\n\n### Output:\n\n**Output Type(s):** \n- Optical Flow Model: Dense optical flow map (2 channels)\n- Stereo Matching Model: Disparity map (1 channel)\n\n**Output Format:** Float32 \n**Output Parameters:** 4D \n**Other Properties Related to Output:** \n\n- The optical flow model outputs a dense flow map with the same dimensions as the input images, containing two channels representing motion in the x and y directions. \n- The stereo matching model outputs a disparity map with the same dimensions as the input images, containing one channel representing the horizontal displacement between corresponding points in the stereo pair.\n\nChannel ordering of the output will be NCHW, where:\n- N = Batch Size\n- C = Number of channels (2 for optical flow or 1 for disparity)\n- H = Height of the output (same as the input)\n- W = Width of the output (same as the input)\n\n### Pre-generated TensorRT Engine\nWe provide pre-generated TensorRT engines for both models with ```versionCompatible``` and ```hardwareCompatibilityLevel=ampere+``` that users can use directly for inference.\nThe engines were built with ```--minShapes=input:1x6x256x256 --optShapes=input:1x6x512x512 --maxShapes=input:1x6x1088x1920```.\nThese compatibility configurations for version/hardware/resolution will make the latency of the current engines 30% - 40% slower compared to engines optimized for a specific environment. \nIf you have requirements for latency optimization tailored to your running environment or for enhancing the quality specific to your scenario, please contact us.\n\nIn the attachments, we also provide Python demos which show:\n1. How to load and deserialize the TensorRT engines\n2. How to get model input/output information\n3. How to prepare input image pairs\n4. How to run inference and get results for both optical flow and stereo matching\n\nPlease refer to the attached Python demos for detailed implementation.\n\n#### Hardware Requirements\nThe pre-generated TensorRT engines require:\n- NVIDIA GPU with Ampere+ architecture\n- TensorRT 10.0 or newer\n\n### Real-time Inference Latency \nThe inference is run on the provided models at FP16 precision. The inference performance is measured using the Python demos listed above, and it does not include the memory copy time between the device and the host. To optimize the inference performance, we have used some custom plugins in the model engines. The Jetson devices are running at Max-N configuration for maximum GPU frequency. The results shown here are the inference performance with input resolution of 1088×1920.\n\n| Platform | NVOF (Optical Flow Model) (ms) | NVOF (Stereo Matching Model) (ms) |\n| :---: | :---: | :---: |\n| RTX4090 | 2.76 | 2.12 |\n| A100 | 4.54 | 3.47 |\n| Jetson Orin | 21.2 | 18.97 |\n\n### License/Terms of Use: \n[NVIDIA Open Model License Agreement](https://developer.download.nvidia.cn/licenses/nvidia-open-model-license-agreement-june-2024.pdf)\n\n## Ethical Considerations: \nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure these models meet requirements for the relevant industry and use case and address unforeseen product misuse.\n\nFor more detailed information on ethical considerations for these models, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.","displayName","NVOF",["D",1763421826486],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/multimedia/models/nvof","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| --- | :---: |\n| Participation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing:| Race, Age, Gender, Skin Color |\n| Measures taken to mitigate against unwanted bias:\t| Our model is designed to estimate the motion between pairs of input images. Therefore, the data used is synthetic data, which includes virtual images of various indoor and outdoor scenes. Our data doesn't require explicit content related to people or religion and so on. To mitigate against unwanted bias, during the process of generation and collection of our data, the richness and diversity of scenes have been taken into account through the adjustment of relevant parameters. |","explainability","| Field | Response |\n| --- | --- |\n| Intended Application(s) & Domain(s): | Building motion analysis capabilities for robotics and autonomous machind |\n| Model Type: | Optical Flow estimation |\n| Intended Users: | Developers who need to conduct motion analysis. |\n| Output: | Continuous Optical Flow Map |\n| Describe how the model works: | The model takes two RGB images as input and produces a dense optical flow image that represents the motion of each pixel between the two frames |\n| Technical Limitations: | The model's motion analysis results may not be 100% accurate, particularly in occluded areas, transparent areas. Significant displacement between the two input images, as well as noticeable changes in lighting or noise, can lead to incorrect motion vector outputs. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | AEPE(average end point error) |\n| Potential Known Risks: | Imprecise motion analysis results can lead to incorrect recognition and understanding of motion |\n| Licensing: | [NVIDIA Open Model License Agreement](https://developer.download.nvidia.cn/licenses/nvidia-open-model-license-agreement-june-2024.pdf) |","privacy","| Field | Response |\n| --- | --- |\n| Generatable or Reverse engineerable personally-identifiable information? | Neither. |\n| Was consent obtained for any PII used? | Yes. |\n| Protected classes used to create this model? | Age, Gender, Race, Skin Colors. |\n| How often is dataset reviewed? | Before Every Release. |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | Yes. |\n| If PII collected for the development of the model, was it collected by NVIDIA? | Yes. |\n| If PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Yes. |\n| If PII collected for the development of this AI model, was it minimised to only what was required? | Yes. Dataset does not contain audio or GPS location data. |\n| Is there provenance for all datasets used in training? | Yes. |\n| Are we able to identify and trace source of dataset? | Yes. |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes. |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes. |\n| Applicable NVIDIA Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy |","safetyAndSecurity","| Field | Response |\n| --- | --- |\n| Model Application(s): | Optical flow estimation. |\n| Describe life critical application (if present): | Potential for life-critical impact in Autonomous Vehicle applications |\n| Use Case Restrictions: | Should not be used for life critical applications. |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538],[377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393],{"_394":500,"_205":500,"_166":167,"_367":501},{"_394":495,"_205":495,"_166":167,"_367":496},{"_394":491,"_205":491,"_166":167,"_367":492},{"_394":486,"_205":486,"_166":167,"_367":487},{"_394":481,"_205":481,"_166":167,"_367":482},{"_394":469,"_205":469,"_166":167,"_367":470},{"_394":457,"_205":457,"_166":167,"_367":458},{"_394":452,"_205":452,"_166":167,"_367":453},{"_394":441,"_205":441,"_166":167,"_367":442},{"_394":436,"_205":436,"_166":167,"_367":437},{"_394":431,"_205":431,"_166":167,"_367":432},{"_394":426,"_205":426,"_166":167,"_367":427},{"_394":421,"_205":421,"_166":167,"_367":422},{"_394":416,"_205":416,"_166":167,"_367":417},{"_394":411,"_205":411,"_166":167,"_367":412},{"_394":404,"_205":404,"_166":167,"_367":405},{"_394":395,"_205":395,"_166":167,"_367":396},"hostName","amazon-adsystem.com",[397,398],{"_77":403,"_400":395,"_401":31,"_166":402},{"_77":399,"_400":395,"_401":31,"_166":402},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[406,407],{"_77":410,"_400":404,"_401":31,"_166":409},{"_77":408,"_400":404,"_401":31,"_166":409},"IDE","This domain is owned by Doubleclick (Google). 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