Object Detection on 3D Point Clouds

Have you heard of Voxel R-CNN?

It's a dope technique for 3D object detection. This is some cutting-edge stuff, and it's important for a few reasons.
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First of all, Voxel R-CNN is a two-stage framework that consists of a 3D backbone network, a 2D bird-eye-view (BEV) Region Proposal Network, and a detect head.
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This means that it's using some serious firepower to get the job done!
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And that's not all!
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Voxel RoI Pooling is also a key feature of this technique. It's designed to extract RoI features directly from raw features for further refinement.
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This is important because it allows for more accurate object detection, which is crucial in applications like autonomous vehicles.
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So, how does it all work?
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Well, the point clouds are first divided into regular voxels and fed into the 3D backbone network for feature extraction.
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Then, the 3D feature volumes are converted into BEV representation, on which the 2D backbone and RPN are applied for region proposal generation.
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Finally, Voxel RoI Pooling directly extracts RoI features from the 3D feature volumes, which are then exploited in the detect head for further box refinement.

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Autoencoders Demystified

Did you know that autoencoders were one of the first wins when it comes to generating images from latent spaces?

Here’s a small overview of them.

An autoencoder is a neural network that’s composed of 2 parts: an encoder and a decoder.

The output of the encoder is called a latent vector.

This same vector is used as an input to the decoder.

Here’s how an autoencoder handles a data point such as an image from start to finish:

1 - First, it takes the image and passes it through a series of conv layers followed by flattening and fully connected layers. This is the encoder

2 - The output of the first part is a vector, let’s call it “z”.

3 - “z” is used as an input to the decoder, which contains a series of transposed conv layers that grow the size of “z” until it’s a full image.

➡ Here are some notes:

“z” is also called an embedding. A term that we often hear when we talk about generative models such as GPT3.

It’s called like this because it embeds information found in the original input image into a smaller space.

➡ There are 2 main kinds for this type of generative AI:

• Autoencoders.
• Variational autoencoders (VAE).

The difference between the two is:

• For autoencoders, the input is mapped to a single point in the latent space.
• For VAEs, the input is mapped to a probability distribution (usually a Gaussian distribution).

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News of the day: ChatGPT API is out!

OpenAI @OpenAI ChatGPT and Whisper are now available through our API (plus developer policy updates). We ❤️ developers: openai.com/blog/introduci… Introducing ChatGPT and Whisper APIs Developers can now integrate ChatGPT and Whisper models into their apps and products through our API... openai.com March 1st 2023 2,447 Retweets 9,602 Likes

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(Fake) Quote of the day by ChatGPT

What'd you think of today's edition?

• ​🤖🤖🤖🤖🤖 Absolutely Great!​
• ​🤖🤖🤖 Meh, do better​
• ​🤖 You didn't bring the heat​