Data augmentations are commonly used to increase the robustness of deep neural networks. In most contemporary research, the networks do not decide the augmentations; they are task-agnostic, and grid search determines their magnitudes. Furthermore, augmentations applicable to lower-dimensional data do not easily extend to higher-dimensional data and vice versa. This paper presents an auto-augmenter for images and meshes (AIM) that easily incorporates into neural networks at training and inference times. It Jointly optimizes with the network to produce constrained, non-rigid deformations in the data. AIM predicts sample-aware deformations suited for a task, and our experiments confirm its effectiveness with various networks.

Unlike images, meshes are irregular and unstructured. Thus, it is not trivial to extend existing image-based deep learning approaches for mesh analysis. In this paper, inspired by dilated convolutions for images, we proffer dilated convolutions for meshes. Our Dilated Mesh Convolution (DMC) unit inflates the kernels’ receptive field without increasing the number of learnable parameters. We also propose a Stacked Dilated Mesh Convolution (SDMC) block by stacking DMC units. It considers spatial regions around mesh faces’ at multiple scales while summarizing the neighboring contextual information. We accommodated SDMC in MeshNet to classify 3D meshes. Experimental results demonstrate that this redesigned model significantly improves classification accuracy on multiple data sets. Code is available at https://github.com/VimsLab/DMC.

Polygon meshes are a popular representation in computer graphics. They efficiently provide delineations of complex 3D shapes. However, their irregular structure hinders mesh analysis efforts in deep learning frameworks; few neural networks exist to describe meshes. MeshNet is a pioneer in this direction. In this paper, we propose a novel neural network that is substantially deeper than its MeshNet predecessor. This increase in depth is achieved through our specialized convolution and pooling blocks that operate on mesh faces. Our network named MeshNet++ learns local structures at multiple scales and is also robust to shortcomings of mesh decimation. We evaluated it for the shape classification task on various data sets, and results significantly higher than state-of-the-art were observed. In particular, results demonstrated that even a small number of examples suffice for training MeshNet++. Our code is available at https://github.com/VimsLab/MeshNet2.

Analysis of sea ice image rasters presents exciting challenges to the computer vision community, and results from such visual analysis can offer novel insights to domain experts studying sea ice. In recent years, there is a surge in the production of geospatial data sets on sea ice along with the methods for their analysis. As numerous providers independently provision geospatial data sets, they are found in varying formats. Also, there is often limited collaboration between the intra-domain and inter-domain experts analyzing these data sets. To tackle these aforementioned challenges, we have developed the cPSITRES system using open-source technologies and libraries to serve cruise-based high-resolution spatiotemporal Big Data on sea ice along with relevant unstructured public data. The system enables its users to perform complex analysis on the provided data sets and also serves as a collaborative platform for the rapid discovery of open-source methods analyzing sea ice. We also showcase a few exemplary traditional and state-of-the-art computer vision methods for the analysis of data provided by cPSITRES.