Recast/Detour VS tensorflow

RecastNavigation

Detour is open source (zlib) so anyone has access to a proven RVO implementation. I believe Unreal and Unity still use Recast/Detour for navmesh and avoidance, but it looks like Godot cooked up their own.

https://github.com/recastnavigation/recastnavigation There is also a library called Yuka that is web based that can generate navmeshes i think: https://mugen87.github.io/yuka/docs/NavMesh.html

Hi, wanted to post about my rust project. I created Rust Lang bindings to the famous https://github.com/recastnavigation/recastnavigation . My library, Divert, currently provides the resources to interact with the Detour, 3d mesh path finding, capabilities of Recast Navigation. In the future, I hope to implement bindings for the Recast side of RecastNavigation which facilitates generating navigation meshes from input geometry to be used by detour. Most of my hobby work revolves around reverse engineering video games, so I needed the capabilities of Detour for my Rust program to navigate in game geometry. I would characterize myself as quite new to Rust still, so constructive criticism is highly welcome. I imagine, I did some really weird stuff while designing this crate, and I bet someone can let me know here ;)

Recast & Detour for navigation meshes (though Rust does have Detour bindings)

For path finding there is at least https://github.com/recastnavigation/recastnavigation

Recast is used in Unreal and many other games.

https://github.com/recastnavigation/recastnavigation Would be.

# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")

[3]: https://github.com/tensorflow/tensorflow/graphs/contributors

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Explore here.

Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.

Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

Exactly what language do you think TensorFlow is written in? :)