• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

• Deep Learning on Point Sets for 3D Classification and Segmentation.

• 3D Convolutional Neural Networks for Landing Zone Detection from LiDAR.

• 1 Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks.
• 2 Learning objects from RGB-D sensors using point cloud-based neural networks.
• 3 3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels
• 4 Orientation-boosted Voxel Nets for 3D Object Recognition
• 5 3D Fully Convolutional Network for Vehicle Detection in Point Cloud

• Awesome Deep Vision. A curated list of deep learning resources for computer vision.
• Deep Learning: An MIT Press book in preparation.
• Deep Learning Reading List. A list of books, papers, surveys and other readings about deep learning.
• Deep down the rabbit hole: CVPR 2015 and beyond. Insightful review of the CVPR2015, including ConvNets, Torch vs Caffe, object detectors, and ArXiv publishing.
• CVPR2015 recap and where we are going. Nice summary of the whole conference, as well as a cool summary/highlights document of some selected papers.

• Introduction to Deep Learning. Video/Slides/Hands-on lab/Office hours Q&A.
• Getting started with the Caffe Framework. Video/Slides/Hands-on lab/Office hours Q&A.

Check out the nice Stanford CS course titled CS231n: Convolutional Neural Networks for Visual Recognition. There is a nice lecture on Convolutional Neural Networks (CNNs / ConvNets) which provides an architectural overview as well as detailed explanations of the different layers and popular architectures.

A nice collection of final reports by the course students is available here. The following ones are specially remarkable:

• Tiny ImageNet Challenge - Dissection of a convolutional neural network. Explains a simple architecture and dissects it with useful insight. Besides, the figures are nice and can serve as inspiration.
• Learning 3D Object Orientations From Synthetic Images. Cool approach to mix 3D training data (which is converted to 2D) with 2D input images for object orientation detection.
• Convolutional Neural Networks for Fashion Classification and Object Detection. Multiple object detection in the same image, instead of single object per image.

Torch is a scientific computing framework which supports a wide variety of machine learning algorithms. Characterized by an easy use thanks to its LuaJIT scripting language. It also features significant efficiency due to the underlying CUDA/C implementation.

• GitHub repository
• Convolutional Neural Network example with Lua
• Object detection with Recursive Convolutional Neural Network and Lua

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). It is implemented mainly in C++ and supports GPU computing thanks to CUDA.

• GitHub repository
• arXiv / ACM MM ‘14 paper. Caffe: Convolutional Architecture for Fast Feature Embedding
• Hands-On Tutorial: Deep Learning for Vision with Caffe

• Awesome TensorFlow
• First contact with TensorFlow
• Understanding neural networks with TensorFlow Playground