TOtodsacerdoti·Dec 16, 2025Submission
Normals_revisited: Revisiting a known normal transformation in computer graphic
2 points · Open discussiongraphitemaster
normals_revisited
Creative & Media2020–2025
graphitemaster / normals_revisited
normals_revisited
revisiting a known normal transformation in computer graphics
2Distinct people
2Mentions
0Explicit recommenders
329GitHub stars
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What it is
revisiting a known normal transformation in computer graphics
GitHub
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
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Mentions over time
HN mentions by year2 dated mentions
Observed mentions: 2020-09-07 18:56:57 UTC to 2025-12-16 11:54:08 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
2 sourcesIt makes perfect sense without GA -- a precalculated normal vector is effectively a stored cross product (this is what the "bivector" terminology is attempting to explain, but is unclear). While the cross product is invariant under rotation and translation, scaling the inputs of the cross product is not the same as scaling the output -- you need to apply the inverse scaling to the output (you can derive this from basic identities).
The "inverse transpose" is a symbolic manipulation to derive a transformation matrix that inverts the scale, but not the rotation, though it's actually a bit overzealous, you can actually just use the cofactor matrix. https://github.com/graphitemaster/normals_revisited
In Geometric Algebra for Python