Skip to main content

Differentiable rendering without approximation.

Project description

redner is a differentiable renderer that can take the derivatives of rendering output with respect to arbitrary scene parameters, that is, you can backpropagate from the image to your 3D scene. One of the major usages of redner is inverse rendering (hence the name redner) through gradient descent. What sets redner apart are: 1) it computes correct rendering gradients stochastically without any approximation and 2) it has a physically-based mode – which means it can simulate photons and produce realistic lighting phenomena, such as shadow and global illumination, and it handles the derivatives of these features correctly. You can also use redner in a fast deferred rendering mode for local shading: in this mode it still has correct gradient estimation and more elaborate material models compared to most differentiable renderers out there.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

redner_gpu-0.4.28-cp38-cp38-manylinux1_x86_64.whl (31.9 MB view details)

Uploaded CPython 3.8

redner_gpu-0.4.28-cp37-cp37m-manylinux1_x86_64.whl (31.8 MB view details)

Uploaded CPython 3.7m

redner_gpu-0.4.28-cp36-cp36m-manylinux1_x86_64.whl (31.8 MB view details)

Uploaded CPython 3.6m

File details

Details for the file redner_gpu-0.4.28-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: redner_gpu-0.4.28-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 31.9 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.7

File hashes

Hashes for redner_gpu-0.4.28-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c3f32bfe81473e21b5bc3ef6754d5af73d22f4e208e181682a5eabe047e0b476
MD5 9d6170451dd86d5ac7e3df7d3eeeed8a
BLAKE2b-256 c8b0b688f8afca02048fb1c8a2d879e5bd72d1261f7838b17c3f84235b3e9a75

See more details on using hashes here.

File details

Details for the file redner_gpu-0.4.28-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: redner_gpu-0.4.28-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 31.8 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.7

File hashes

Hashes for redner_gpu-0.4.28-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 6d0b421f6fed6e5d8b8a9d6ceba64f5151dbe8c4c7f0ea57763fccbcc278725b
MD5 680c104b4d397b109bb75c8420862563
BLAKE2b-256 c7d9f1437e720fdb73162d640a1c99026a9bec1ed5e55ec57e8ea33bd626825c

See more details on using hashes here.

File details

Details for the file redner_gpu-0.4.28-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: redner_gpu-0.4.28-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 31.8 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.7.7

File hashes

Hashes for redner_gpu-0.4.28-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 5c89063df6ec0bdea55a17cfa076f9d0dd65a7018220095aeebc59f6f54b8637
MD5 7a034d24d4a4ebedba12c49324b597e1
BLAKE2b-256 a469cfa8b38c9ba69436247d14ac2cb57c0f30057f9ac607ab1fdff06bd6a159

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page