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1. A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION

   https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022  

   Nurunnabi, A; Teferle, F N; Laefer, D F; Lindenbergh, R C; Hunegnaw, A (January 2022, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient feature management can reduce storage and computational complexities, builds better classifiers, and improves overall performance. Principal Component Analysis (PCA) is a well-known dimension reduction technique that has been used for feature extraction. This paper presents a two-step PCA based feature extraction algorithm that employs a variant of feature-based PointNet (Qi et al., 2017a) for point cloud classification. This paper extends the PointNet framework for use on large-scale aerial LiDAR data, and contributes by (i) developing a new feature extraction algorithm, (ii) exploring the impact of dimensionality reduction in feature extraction, and (iii) introducing a non-end-to-end PointNet variant for per point classification in point clouds. This is demonstrated on aerial laser scanning (ALS) point clouds. The algorithm successfully reduces the dimension of the feature space without sacrificing performance, as benchmarked against the original PointNet algorithm. When tested on the well-known Vaihingen data set, the proposed algorithm achieves an Overall Accuracy (OA) of 74.64% by using 9 input vectors and 14 shape features, whereas with the same 9 input vectors and only 5PCs (principal components built by the 14 shape features) it actually achieves a higher OA of 75.36% which demonstrates the effect of efficient dimensionality reduction. 

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2. Generative PointNet: deep energy-based learning on unordered point sets for 3D generation, reconstruction and classification

   Xie, J.; Xu, Y.; Zheng, Z.; Zhu, S. C.; Wu, Y. N. (January 2021, IEEE Conference on Computer Vision and Pattern Recognition)

   We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input permutation- invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual point features into an energy for the whole point cloud. We call our model the Generative PointNet because it can be derived from the discriminative PointNet. Our model can be trained by MCMC based maximum likelihood learning (as well as its variants), without the help of any assisting networks like those in GANs and VAEs. Unlike most point cloud generators that rely on hand-crafted distance metrics, our model does not require any hand-crafted distance metric for the point cloud generation, because it synthesizes point clouds by matching observed examples in terms of statistical properties defined by the energy function. Furthermore, we can learn a short run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpolation. The learned point cloud representation can be useful for point cloud classification. Experiments demonstrate the advantages of the proposed generative model of point clouds. 

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3. Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks

   Wang, He; Jiang, Zetian; Yi, Li; Mo, Kaichun; Su, Hao; Guibas, Leonidas (January 2021, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop)

   null (Ed.)

   In this paper, we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs. Through extensive experiments, we show that sampling-insensitive discriminators (e.g.PointNet-Max) produce shape point clouds with point clustering artifacts while sampling-oversensitive discriminators (e.g. PointNet++, DGCNN) fail to guide valid shape generation. We propose the concept of sampling spectrum to depict the different sampling sensitivities of discriminators. We further study how different evaluation metrics weigh the sampling pattern against the geometry and propose several perceptual metrics forming a sampling spectrum of metrics. Guided by the proposed sampling spectrum, we discover a middle-point sampling-aware baseline discriminator, PointNet-Mix, which improves all existing point cloud generators by a large margin on sampling-related metrics. We point out that, though recent research has been focused on the generator design, the main bottleneck of point cloud GAN actually lies in the discriminator design. Our work provides both suggestions and tools for building future discriminators. We will release the code to facilitate future research. 

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4. Local-environment-guided selection of atomic structures for the development of machine-learning potentials

   https://doi.org/10.1063/5.0187892  

   Li, Renzhe; Zhou, Chuan; Singh, Akksay; Pei, Yong; Henkelman, Graeme; Li, Lei (February 2024, The Journal of Chemical Physics)

   Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs. 

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5. Dynamic Reflections Using Eye-Resolution Point Rendering

   Gavane, A; Watson, B.A. (July 2021, Highperformance graphics)

   We describe an improvement to the recently developed view independent rendering (VIR), and apply it to dynamic cube-mapped reflections. Standard multiview rendering (MVR) renders a scene six times for each cube map. VIR traverses the geometry once per frame to generate a point cloud optimized to many cube maps, using it to render reflected views in parallel. Our improvement, eye-resolution point rendering (EPR), is faster than VIR and makes cube maps faster than MVR, with comparable visual quality. We are currently improving EPR’s run time by reducing point cloud size and per-point processing. 

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