How to Install and Uninstall bcd.x86_64 Package on Fedora 34

Last updated: June 29,2024

1. Install "bcd.x86_64" package

In this section, we are going to explain the necessary steps to install bcd.x86_64 on Fedora 34

$ sudo dnf update $ sudo dnf install bcd.x86_64

2. Uninstall "bcd.x86_64" package

Learn how to uninstall bcd.x86_64 on Fedora 34:

$ sudo dnf remove bcd.x86_64 $ sudo dnf autoremove

3. Information about the bcd.x86_64 package on Fedora 34

Last metadata expiration check: 1:21:54 ago on Tue Sep 6 02:10:55 2022.
Available Packages
Name : bcd
Version : 1.1
Release : 4.20180610gitd94c9fa.fc34
Architecture : x86_64
Size : 135 k
Source : bcd-1.1-4.20180610gitd94c9fa.fc34.src.rpm
Repository : fedora
Summary : Bayesian Collaborative Denoiser for Monte-Carlo Rendering
URL : https://github.com/superboubek/bcd
License : BSD and AGPLv3+
Description : BCD allows to denoise images rendered with Monte Carlo path tracing and
: provided in the form of their samples statistics (average, distribution
: and covariance of per-pixel color samples). BCD can run in CPU (e.g.,
: renderfarm) or GPU (e.g., desktop) mode. It can be integrated as a library
: to any Monte Carlo renderer, using the provided sample accumulator to
: interface the Monte Carlo simulation with the BCD internals, and comes
: with a graphics user interface for designing interactively the denoising
: parameters, which can be saved in JSON format and later reused in batch.
:
: BCD has been designed for easy integration and low invasiveness in the
: host renderer, in a high spp context (production rendering). There are
: at least three ways to integrate BCD in a rendering pipeline, by either:
:
: * Dumping all samples in a raw file, using the raw2bcd tool to generate
: the rendering statistics from this file and then running the BCD using
: the CLI tool.
:
: * Exporting the mandatory statistics from the rendering loop in EXR
: format and running the BCD CLI tool to obtain a denoised image.
:
: * Directly integrating the BCD library into the renderer, using the
: sample accumulator to post samples to BCD during the path tracing and
: denoising the accumulated values after rendering using the library.