VPI-3.0.10

VPI-3.0.10 is the first production release of VPI-3.0 branch. It contains several new algorithms and bug fixes.

Documentation

Link to documentation for release VPI-3.0

Release Highlights

• New algorithms:
  • Crop Scaler implemented in CUDA backend.
  • DCF Tracker implemented in CUDA and PVA backends.
• Brute Force Matcher :
  • Added implementation on CUDA backend.
  • Significant improved performance on CPU backend by making use of all CPU cores available and further improvements in algorithm complexity when doing cross-check.
• Dense Optical Flow :
  • Expose more parameters from SGM algorithm OFA implementation, to allow fine-tuning of output quality. See VPIOpticalFlowDenseSGMParams and related new APIs.
  • Increased supported pyramid levels from 5 to 7, allowing detection of flow across larger areas.
• Python API:
  • Binaries for python 3.9 and 3.10.
  • Deployment of VPI's python source code so that it can be compiled for other Python versions.
• Supported x86 platforms:
  • x86 Ubuntu 20.04 and 22.04
  • Jetson boards:
    • AGX Orin DevKit
    • AGX Orin 32/64GB/JAOi
    • Orin NX 16/8GB
    • Orin Nano 8/4GB
    • Orin Nano DevKit

Other Updates

• Colornames:
  • Added PVA backend, which accepts RGB8p input image format.
• HOG:
  • Up to 2x faster performance of CUDA backend.
• Added support for several RGB planar image formats like RGBA8p, RGB8p, BGRA8p and BGR8p. The following algorithms were updated:
  • Box Filter
  • Convolution
  • Gaussian Filter
  • Gaussian Pyramid Generator
  • Image Statistics
  • Median Filter
  • Mix Channels
  • Rescale
• Gaussian Pyramid Generator :
  • VPI_BORDER_REFLECT has now been added.

Selected Bug Fixes

• Fixed segfault with python bindings when importing modules like torch after vpi's.

Removed Features

• Temporal Noise Reduction :
  • TNRv2 on VIC backend is no longer supported.
• Stereo Disparity Estimator :
  • Removed the following backends:
    • CPU-only, PVA-only, PVA+NVENC+VIC.
• GUI demos have been removed.
• Xavier devices are no longer supported.
• Dropped support for CUDA-11.x. Only CUDA 12.x is supported now.
• Dropped support for Ubuntu 18.04

Known Issues

• Host images wrapped into VPIImages using vpiImageCreateWrapper might incur in a performance hit when using them with algorithms running on CUDA backend. User should avoid wrappers in this case, preferring to use VPIImages allocated with vpiImageCreate.
• Possible performance hit when using CUDA images wrapped into VPIImages using vpiImageCreateWrapper in algorithms running in PVA, VIC and/or OFA. User should avoid using wrappers in this case, preferring to use VPIImages allocated with vpiImageCreate.
• Harris Corner Detector result scores/positions might differ among backends.
• Stereo Disparity Estimator
  • The confidence map generated by OFA+PVA+VIC backend might have some negligible differences with respect to other backends.
• Performance hit using Dense Optical Flow on python due re-creating the payload at every call.
• Per-algorithm performance tables weren't updated, they still refer to performance from vpi-2.0.

• Attention

  Algorithms running on PVA backend won't work inside a docker container. Submission calls will return error VPI_ERROR_INTERNAL .