Stereo Disparity Estimator

Overview

Given a pair of rectified images from a stereo camera, the Stereo Disparity algorithm uses high-quality dense stereo matching to produce an output image of the same resolution as the input with left-right disparity information. This allows for inferring the depth of the scene captured by the left and right images.

Left image	Right image
Disparity map

Implementation

The stereo disparity estimator uses semi-global matching algorithm to compute the disparity. We deviate from the original algorithm by using as cost function the hamming distance of the census transforms of the stereo pair.

Usage

1. Initialization phase
   1. Include the header that defines the needed functions and structures.

      #include <vpi/algo/StereoDisparity.h>

   2. Define the input rectified stereo pair.

          VPIImage left  = /*...*/;
          VPIImage right = /*...*/;

   3. Create the output disparity image.

          uint32_t w, h;
          vpiImageGetSize(left, &w, &h);
       
          VPIImage disparity;
          vpiImageCreate(w, h, VPI_IMAGE_FORMAT_U16, 0, &disparity);

   4. Create the payload that will contain all temporary buffers needed for processing. It'll be created on the CUDA backend.

          VPIPayload stereo;
          vpiCreateStereoDisparityEstimator(VPI_BACKEND_CUDA, 480, 270, VPI_IMAGE_FORMAT_U16, 64, &stereo);

   5. Create the stream where the algorithm will be submitted for execution.

          VPIStream stream;
          vpiStreamCreate(0, &stream);

2. Processing phase
   1. Define the configuration parameters needed for algorithm execution.

          VPIStereoDisparityEstimatorParams params;
          params.windowSize   = 5;
          params.maxDisparity = 64;

   2. Submit the payload for execution on the backend associated with it.

          vpiSubmitStereoDisparityEstimator(stream, stereo, left, right, disparity, &params);

   3. Optionally, wait until the processing is done.

          vpiStreamSync(stream);

3. Cleanup phase
   1. Free resources held by the stream, the payload and the input and ouput images.

          vpiStreamDestroy(stream);
          vpiPayloadDestroy(stereo);
          vpiImageDestroy(left);
          vpiImageDestroy(right);
          vpiImageDestroy(disparity);

Consult the Stereo Disparity Sample for a complete example.

For more details, consult the Stereo Disparity Estimator API reference.

Limitations and Constraints

Constraints for specific backends superceed the ones specified for all backends.

All Backends

• Left and right input images must have same type and dimensions.
• Output image dimensions must match input's.
• Output disparity images must have type VPI_IMAGE_FORMAT_U16.
• Maximum disparity parameter passed to algorithm submission must be the same as defined during payload creation.
• Input image dimensions must match what is defined during payload creation.

CUDA and CPU

• Accepted Left and right input image formats:
  • VPI_IMAGE_FORMAT_U8
  • VPI_IMAGE_FORMAT_U16

PVA

• Only available on Jetson Xavier devices.
• Left and right accepted input image types:
  • VPI_IMAGE_FORMAT_U16
• Input and output image dimensions must be 480x270.
• windowSize must be 5.
• maxDisparity must be 64.

VIC

• Not implemented.

Performance

For information on how to use the performance table below, see Algorithm Performance Tables.
Before comparing measurements, consult Comparing Algorithm Elapsed Times.
For further information on how performance was benchmarked, see Performance Measurement.

clear filters Device: Jetson AGX Xavier Jetson TX2 Jetson Nano  -  Streams: 1 4 8

References

• Hirschmüller, Heiko (2005). "Accurate and efficient stereo processing by semi-global matching and mutual information".
  IEEE Conference on Computer Vision and Pattern Recognition. pp. 807–814.
• Zabih, Ramin; Woodfill, John (1994). "Non-parametric local transforms for computing visual correspondence".
  European conference on computer vision. pp. 151–158.