CubeNet: Equivariance to 3D Rotation and Translation

3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk "diluting" the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.Comment: Preprin

Semantic Segmentation and Completion of 2D and 3D Scenes

Semantic segmentation is one of the fundamental problems in computer vision. This thesis addresses various tasks, all related to the fine-grained, i.e. pixel-wise or voxel-wise, semantic understanding of a scene. In the recent years semantic segmentation by 2D convolutional neural networks has become as much as a default pre-processing step for many other computer vision tasks, since it outputs very rich spatially resolved feature maps and semantic labels that are useful for many higher level recognition tasks. In this thesis, we make several contributions to the field of semantic scene understanding using an image or a depth measurement, recorded by different types of laser sensors, as input. Firstly, we propose a new approach to 2D semantic segmentation of images. It consists of an adaptation of an existing approach for real time capability under constrained hardware demands that are required by a real life drone. The approach is based on a highly optimized implementation of random forests combined with a label propagation strategy. Next, we shift our focus to what we believe is one of the important next forefronts in computer vision: To give machines the ability to anticipate and extrapolate beyond what is captured in a single frame by a camera or depth sensor. This anticipation capability is what allows humans to efficiently interact with their environment. The need for this ability is most prominently displayed in the behaviour of today's autonomous cars. One of their shortcomings is that they only interpret the current sensor state, which prevents them from anticipating events which would require an adaptation of their driving policy. The result is a lot of sudden breaks and non-human-like driving behaviour, which can provoke accidents or negatively impact the traffic flow. Therefore we first propose a task to spatially anticipate semantic labels outside the field of view of an image. The task is based on the Cityscapes dataset, where each image has been center cropped. The goal is to train an algorithm that predicts the semantic segmentation map in the area outside the cropped input region. Along with the task itself, we propose an efficient iterative approach based on 2D convolutional neural networks by designing a task adapted loss function. Afterwards, we switch to the 3D domain. In three dimensions the goal shifts from assigning pixel-wise labels towards the reconstruction of the full 3D scene using a grid of labeled voxels. Thereby one has to anticipate the semantics and geometry in the space that is occluded by the objects themselves from the viewpoint of an image or laser sensor. The task is known as 3D semantic scene completion and has recently caught a lot of attention. Here we propose two new approaches that advance the performance of existing 3D semantic scene completion baselines. The first one is a two stream approach where we leverage a multi-modal input consisting of images and Kinect depth measurements in an early fusion scheme. Moreover we propose a more memory efficient input embedding. The second approach to semantic scene completion leverages the power of the recently introduced generative adversarial networks (GANs). Here we construct a network architecture that follows the GAN principles and uses a discriminator network as an additional regularizer in the 3D-CNN training. With our proposed approaches in semantic scene completion we achieve a new state-of-the-art performance on two benchmark datasets. Finally we observe that one of the shortcomings in semantic scene completion is the lack of a realistic, large scale dataset. We therefore introduce the first real world dataset for semantic scene completion based on the KITTI odometry benchmark. By semantically annotating alls scans of a 10 Hz Velodyne laser scanner, driving through urban and countryside areas, we obtain data that is valuable for many tasks including semantic scene completion. Along with the data we explore the performance of current semantic scene completion models as well as models for semantic point cloud segmentation and motion segmentation. The results show that there is still a lot of space for improvement for either tasks so our dataset is a valuable contribution for future research into these directions

PROTOTIPO FUNCIONAL PARA CLASIFICACIÓN DE IMÁGENES CON SALIDA DE AUDIO EN UN SISTEMA EMBEBIDO CON RED NEURONAL CONVOLUCIONAL (FUNCTIONAL PROTOTYPE FOR CLASSIFICATION OF IMAGES WITH AUDIO OUTPUT IN AN EMBEDDED SYSTEM USING CONVOLUTIONAL NEURAL NETWORK)

En los últimos años, las redes neuronales convolucionales, han tenido una gran popularidad en aplicaciones de clasificación de imágenes, principalmente porque superan en rendimiento a los algoritmos tradicionales. Sin embargo, su alto costo computacional complica su implementación en sistemas embebidos con pocos recursos como las Raspberry Pi 3. Para superar este problema, se puede hacer uso del “Neural Compute Stick”, un dispositivo desarrollado recientemente, que integra una GPU en la que se puede cargar una red neuronal convolucional pre-entrenada. En este artículo se presenta un prototipo basado en la Raspberry Pi 3, que realiza clasificación de imágenes con reproducción de audio. La clasificación se realizada con la red GoogleNet, la cual es entrenada fuera de línea, implementada en un NCS e integrada a la tarjeta Raspberry Pi 3. En el sistema propuesto, la imagen que ingresa a través de una cámara web, es clasificada y etiquetada con la red convolucional y finalmente la etiqueta es traducida en audio por el sistema embebido para describir el objeto encontrado en la imagen.In recent years, convolutional neural networks (CNN) have become very popular in image classification applications, mainly because they outperform traditional algorithms in performance. However, its high computational cost complicates its implementation in embedded systems with few resources such as Raspberry Pi 3. To overcome this problem, a "Neural Compute Stick" (NCS) can be used, which integrates a GPU. In the NCS can be loaded a pre-trained convolutional neural network. This article presents a prototype based on a Raspberry Pi 3, which performs image classification with audio reproduction. The classification is done through a GoogleNet net, which is trained offline, implemented in the NCS and integrated with the Raspberry Pi 3 card. In the proposed system, the image that enters through a webcam is classified and tagged with the CNN. Finally, the tag is translated into an audio file to be heard

Unsupervised Point Cloud Pre-Training via Occlusion Completion

We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the occluded points; 3. Use the encoder weights as initialisation for downstream point cloud tasks. We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks. Specifically, we show that our method outperforms previous pre-training methods in object classification, and both part-based and semantic segmentation tasks. We study the pre-trained features and find that they lead to wide downstream minima, have high transformation invariance, and have activations that are highly correlated with part labels. Code and data are available at: https://github.com/hansen7/OcCoComment: sync with ICCV camera read

On incorporating inductive biases into deep neural networks

A machine learning (ML) algorithm can be interpreted as a system that learns to capture patterns in data distributions. Before the modern \emph{deep learning era}, emulating the human brain, the use of structured representations and strong inductive bias have been prevalent in building ML models, partly due to the expensive computational resources and the limited availability of data. On the contrary, armed with increasingly cheaper hardware and abundant data, deep learning has made unprecedented progress during the past decade, showcasing incredible performance on a diverse set of ML tasks. In contrast to \emph{classical ML} models, the latter seeks to minimize structured representations and inductive bias when learning, implicitly favoring the flexibility of learning over manual intervention. Despite the impressive performance, attention is being drawn towards enhancing the (relatively) weaker areas of deep models such as learning with limited resources, robustness, minimal overhead to realize simple relationships, and ability to generalize the learned representations beyond the training conditions, which were (arguably) the forte of classical ML. Consequently, a recent hybrid trend is surfacing that aims to blend structured representations and substantial inductive bias into deep models, with the hope of improving them. Based on the above motivation, this thesis investigates methods to improve the performance of deep models using inductive bias and structured representations across multiple problem domains. To this end, we inject a priori knowledge into deep models in the form of enhanced feature extraction techniques, geometrical priors, engineered features, and optimization constraints. Especially, we show that by leveraging the prior knowledge about the task in hand and the structure of data, the performance of deep learning models can be significantly elevated. We begin by exploring equivariant representation learning. In general, the real-world observations are prone to fundamental transformations (e.g., translation, rotation), and deep models typically demand expensive data-augmentations and a high number of filters to tackle such variance. In comparison, carefully designed equivariant filters possess this ability by nature. Henceforth, we propose a novel \emph{volumetric convolution} operation that can convolve arbitrary functions in the unit-ball ($\mathbb{B}^3$) while preserving rotational equivariance by projecting the input data onto the Zernike basis. We conduct extensive experiments and show that our formulations can be used to construct significantly cheaper ML models. Next, we study generative modeling of 3D objects and propose a principled approach to synthesize 3D point-clouds in the spectral-domain by obtaining a structured representation of 3D points as functions on the unit sphere ($\mathbb{S}^2$). Using the prior knowledge about the spectral moments and the output data manifold, we design an architecture that can maximally utilize the information in the inputs and generate high-resolution point-clouds with minimal computational overhead. Finally, we propose a framework to build normalizing flows (NF) based on increasing triangular maps and Bernstein-type polynomials. Compared to the existing NF approaches, our framework consists of favorable characteristics for fusing inductive bias within the model i.e., theoretical upper bounds for the approximation error, robustness, higher interpretability, suitability for compactly supported densities, and the ability to employ higher degree polynomials without training instability. Most importantly, we present a constructive universality proof, which permits us to analytically derive the optimal model coefficients for known transformations without training