# Demos & Showcases **URL:** \ **Category:** Demos and use cases\ **Created:** [July 5, 2021, 2:18pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16 "2021-07-05T14:18:59Z")\ **Posts on this page:** 13\ **Page:** 1
**Author:** ![rapidcompact](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/rapidcompact/32/13_2.png) [@rapidcompact](https://forum.rapidcompact.com/u/rapidcompact)\ **Post date:** [July 5, 2021, 2:18pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/1 "2021-07-05T14:18:59Z")
Share with us some of the results you generated with RapidCompact! ---
**Author:** ![mlimper](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/mlimper/32/26_2.png) [@mlimper](https://forum.rapidcompact.com/u/mlimper)\ **Post date:** [July 5, 2021, 2:56pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/2 "2021-07-05T14:56:53Z")
I’ll do one of our standard examples, to have sth to start with 🙂 This armchair is originally from Turbosquid and has **878K triangles / 58 MB** : ![image](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/e366aacd257e2f316b390aeab298e1379728fda1.jpeg) One demo version of the same asset, reduced by RapidCompact using UV-preserving simplification, has **220K Triangles (~25%) / 2.4 MB**, and I believe it is visually nearly identical to the original: ![image](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/0664cd65dd89656a82c384d43f29cdf74ebbae9a.jpeg) As you can see, the file size is already in a pretty neat range, which was possible due to the UV preserving simplification that allowed the texture maps being tiled, even in the simplified version, and hence we could keep a lot of detail without requiring too much resolution. Also, 100KB were consumed by the transparent texture of that that “soft shadow” plane, which was somehow modeled into the original asset (and could have been removed): ![image](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/593d76b7751dc95ef7d41fb5e24970a0843f8ce1.jpeg) IIRC, what required the still pretty high resolution, in order to have good-looking output, were all these little strands from the piece of cloth. Asset size could be further reduced by applying basis compression (using the glTF KTX2 container format) to the texture maps, and I’m not sure if we used draco… but anyway, 2.4MB is a nice size for this kind of asset, guess improvement should go into the geometry reduction (seeing how far we can go down before it breaks), in order to provide a more fluent interaction on devices with weaker GPUs 🙂 ---
**Author:** ![M\_Arnold](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/m_arnold/32/18_2.png) [@M\_Arnold](https://forum.rapidcompact.com/u/M_Arnold)\ **Post date:** [July 6, 2021, 8:29am UTC](https://forum.rapidcompact.com/t/demos-showcases/16/3 "2021-07-06T08:29:20Z")
To start my internship at DGG and to get to know RapidCompact better I tried to optimize the following bread model. The original file has **266K faces** and **12.2 MB**. ![Brot-orig](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/65c888265ed215a181644c3f92911df7729e0694.png) I tried to reach a megabyte target of under 100 KB without major inaccuracies. With a mesh resolution of 3% of the faces, switching of all the textures except the base color map and a ktx2 texture compression the result was **6670 faces** and **99.7 KB**. ![Brot-output](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/c3055273ea8fa3bbc313bb7326640bd04a4d576e.png) ---
**Author:** ![mlimper](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/mlimper/32/26_2.png) [@mlimper](https://forum.rapidcompact.com/u/mlimper)\ **Post date:** [July 6, 2021, 9:37am UTC](https://forum.rapidcompact.com/t/demos-showcases/16/4 "2021-07-06T09:37:33Z")
\< 100KB is quite impressive (especially since it still looks usable^^) Thanks for sharing this! ---
**Author:** ![Felix](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/felix/32/9_2.png) [@Felix](https://forum.rapidcompact.com/u/Felix)\ **Post date:** [July 7, 2021, 8:50am UTC](https://forum.rapidcompact.com/t/demos-showcases/16/5 "2021-07-07T08:50:19Z")
Hi there, would it be possible to see the example in 3D? It is difficult to see the quality with only an image. Thanks for sharing!! ---
**Author:** ![mlimper](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/mlimper/32/26_2.png) [@mlimper](https://forum.rapidcompact.com/u/mlimper)\ **Post date:** [July 7, 2021, 9:48am UTC](https://forum.rapidcompact.com/t/demos-showcases/16/6 "2021-07-07T09:48:14Z")
Couldn’t resist to share another quick one - this one mainly benefits from texture optimization ( **131.61MB =\> 5.42MB** ). Cool thing is, it is actually animated (has rigid transform animations, which are preserved by the optimizer) 🙂 ![image](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/fb53c45531976155090e93aebd5491b3d782d5c7.jpeg) Live demo: [https://api.rapidcompact.com/viewer?id=5sWVCHilSP](https://api.rapidcompact.com/viewer?id=5sWVCHilSP) ---
**Author:** ![DGGchris](https://avatars.discourse-cdn.com/v4/letter/d/f05b48/32.png) [@DGGchris](https://forum.rapidcompact.com/u/DGGchris)\ **Post date:** [July 8, 2021, 12:24pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/7 "2021-07-08T12:24:48Z")
Here is an example of a model optimized on our **cloud platform** with a **preset** to hit the optimal requirements for a specific software usecase. (cloud preview image of the input) ![image](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/0384b3075b882827e848ab5b5e2ef0137eafc994.jpeg) Input Model **5.13 MB & 22.700 faces** (with Adobe Aero Preset) =\> **1.41 MB & 22.700 faces** This is a great example of how much optimization is possible even without touching the face count. Adobe Aero ready asset: [https://api.rapidcompact.com/viewer?id=UJuXOIyG9l](https://api.rapidcompact.com/viewer?id=UJuXOIyG9l) ---
**Author:** ![M\_Arnold](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/m_arnold/32/18_2.png) [@M\_Arnold](https://forum.rapidcompact.com/u/M_Arnold)\ **Post date:** [July 8, 2021, 2:56pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/9 "2021-07-08T14:56:36Z")
Sure! The first link is for the original: [https://rapidcompact.com/demo/Brot/index.html?modelURL=scene.gltf](https://rapidcompact.com/demo/Brot/index.html?modelURL=scene.gltf) and this is the small version: [https://rapidcompact.com/demo/Brot/index.html?modelURL=scene-small.glb](https://rapidcompact.com/demo/Brot/index.html?modelURL=scene-small.glb) ---
**Author:** ![rapidcompact](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/rapidcompact/32/13_2.png) [@rapidcompact](https://forum.rapidcompact.com/u/rapidcompact)\ **Post date:** [March 31, 2022, 6:16pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/10 "2022-03-31T18:16:27Z")
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**Author:** ![teresa\_dgg](https://avatars.discourse-cdn.com/v4/letter/t/848f3c/32.png) [@teresa\_dgg](https://forum.rapidcompact.com/u/teresa_dgg)\ **Post date:** [May 11, 2022, 12:19pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/11 "2022-05-11T12:19:23Z")
The case of last Model of the Month was quite impressive: using the DropTextures Workflow, it went from 150 MB to 0.85 MB. Apart from texture simplification, the model allowed quite a lot of decimation, as it was hard-surface and had big surfaces. [The 3D Embed](https://api.rapidcompact.com/viewer?id=tiOnKlCo9f) ![01_original](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/b0dc51b8f2f1b6fcaf51522d258cbed37a2608ba.jpeg) ![01_compacted](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/2a0c9075dd578d7da60c7822bf31e943977888b0.jpeg) ---
**Author:** ![daniel\_mutuma](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/daniel_mutuma/32/74_2.png) [@daniel\_mutuma](https://forum.rapidcompact.com/u/daniel_mutuma)\ **Post date:** [June 10, 2022, 12:38pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/13 "2022-06-10T12:38:50Z")
Here is another impressive result I got with Rapid Compact. This original model was **312 MB** ![original_version](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/609fb4dff379768d89a002c1534a30b905f1f409.jpeg) Here is the optimised version coming in at **5.3MB**. Despite optimising the model it still looks as good as the original. ![optimised_version](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/7aa9d47fa1f994062e75615ac85de626bf9be733.jpeg) Link to 3D viewer : [RapidCompact 3D Viewer](https://next.rapidcompact.cloud/viewer?id=mpLnjtFLjl) ---
**Author:** ![daniel\_mutuma](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/daniel_mutuma/32/74_2.png) [@daniel\_mutuma](https://forum.rapidcompact.com/u/daniel_mutuma)\ **Post date:** [June 10, 2022, 2:10pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/15 "2022-06-10T14:10:39Z")
I wanted to play around with this sci fi helmet in AR but its original version was not optimised for augmented reality. I used the Hololens preset to take it from 32 MB to 1.75 MB. I decided to try my own custom preset and reduce it to 1.68 MB. The optimised version of the helmet maintained the details impressively well. ![original version](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/b198a2408cddab3b0c938bb20aa6082b1faa76d2.jpeg) ![optimised version](https://europe1.discourse-cdn.com/flex017/uploads/rapidcompact/original/1X/0609ac7b7ddc4f76caac4416829e967f3af6f5ff.jpeg) [RapidCompact 3D Viewer](https://api.rapidcompact.com/viewer?id=uQOkZp2WhU) As you can see there isn’t much of a difference 🤯 ---
**Author:** ![mlimper](https://dub1.discourse-cdn.com/flex017/user_avatar/forum.rapidcompact.com/mlimper/32/26_2.png) [@mlimper](https://forum.rapidcompact.com/u/mlimper)\ **Post date:** [June 10, 2022, 2:42pm UTC](https://forum.rapidcompact.com/t/demos-showcases/16/16 "2022-06-10T14:42:14Z")
very nice, thanks for sharing 🙂