TensorFlow 3D: Deep Learning for Autonomous Cars’ 3D Perception

TensorFlow 3D: Deep Learning for Autonomous Cars’ 3D Perception

Google has released TensorFlow 3D, a library that adds 3D deep-learning capabilities to the TensorFlow machine-learning framework. The new library brings tools and resources that allow researchers to develop and deploy 3D scene understanding models.

Google has released TensorFlow 3D, a library that adds 3D deep-learning capabilities to the TensorFlow machine-learning framework. The new library brings tools and resources that allow researchers to develop and deploy 3D scene understanding models. 

TensorFlow 3D contains state-of-the-art models for 3D deep learning with GPU acceleration. These models have a wide range of applications from 3D object detection (e.g. cars, pedestrians,etc) to point cloud’s registration (e.g. 3D indoors mapping). 

For instance, 3D object detection is a hard problem using point cloud data due to high sparsity. To make it easier to build efficient models, TensorFlow 3D provides sparse convolution network and pooling operations. We can imagine convolution networks in 3D as a method to handle sparsity in this data. Similarly, one can think of a convolution network for an image with only a few non-zero pixels.

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