We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs). Our method builds hierarchical hash tables for an input model under different resolutions that leverage the sparse occupancy of 3D shape boundary. Based on this data structure, we design two efficient GPU algorithms namely hash2col and col2hash so that the CNN operations like convolution and pooling can be efficiently parallelized. The perfect spatial hashing is employed as our spatial hashing scheme, which is not only free of hash collision but also nearly minimal so that our data structure is almost of the same size as the raw input. Compared with existing 3D CNN methods, our data structure significantly reduces the memory footprint during the CNN training. As the input geometry features are more compactly packed, CNN operations also run faster with our data structure. The experiment shows that, under the same network structure, our method yields comparable or better benchmark results compared with the state-of-the-art while it has only one-third memory consumption when under high resolutions (i.e. 256 3).
3D shape analysis @cite_47 @cite_26 @cite_18 is one of the most fundamental tasks in computer graphics. Most existing works utilize manually crafted features for dedicated tasks such as shape retrieval and segmentation. Encouraged by great successes in 2D images analysis using CNN-based machine learning methods @cite_39 @cite_2 @cite_50 , many research efforts have been devoted to leverage CNN techniques for 3D shape analysis.
@cite_18 @cite_26 @cite_39 @cite_50 @cite_2 @cite_47
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The majority of man-made objects are designed to serve a certain function, and this is often reflected by the geometry of the objects, or the way that they are used or organized in an environment. In recent years, many efforts in shape analysis have developed methods that extract high-level structural and semantic information from geometric shapes and scenes, especially involving man-made objects. One can argue that the ultimate goal of some of these works is to understand the functionality of the objects. Moreover, there have also been works that explicitly model and incorporate functionality into the processing of shapes and scenes. Thus, functionality has been receiving increasingly more attention in shape analysis and geometric modeling, either directly or indirectly, since functionality considerations can aid in applications such as semantic classification, shape editing and synthesis, as well as product design, development, and fabrication. In this course, we discuss recent developments that incorporate functionality aspects into the analysis of 3D shapes and scenes, to provide a summary of the state-of-the-art in this area, including a discussion of key ideas and literature works. More specifically, we first discuss approaches that are precursors in this front, such as structure-aware and data-driven methods that learn relationships between shape parts or objects in scenes. Next, we cover works that more explicitly model the functionality of shapes and scenes, such as agent- and interaction-based methods. The course is structured in the form of talks given by four different speakers, aided by electronic slides that include notes for subsequent consultation. Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing. Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the network which was trained to perform object classification on ILSVRC13. We use features extracted from the network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. The results are achieved using a linear SVM classifier (or @math distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. The representations are further modified using simple augmentation techniques e.g. jittering. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks. Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94 top-5 test error on the ImageNet 2012 classification dataset. This is a 26 relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66 [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1 , [26]) on this dataset.