A survey on deep geometry learning: From a representation perspective

Xiao, Yanfeng; Lai, Yuekun; Zhang, Fang-Lue; Li, Chunpeng; Gao, Lin

doi:10.1007/s41095-020-0174-8

Cited by 93 publications

(52 citation statements)

References 78 publications

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“…Compared to their tasks, our goal focuses on general mesh sequences, which have higher dimensions and are more general than skeleton data. More research works on the 3D shape generation could be referred to [43]; 3D Shape Interpolation. Compared with image generation, synthesizing 3D shapes is more challenging due to the high dimensionality and irregular connectivity of mesh data.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing Mesh Deformation Sequences With Bidirectional LSTM

Qiao

Lai

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Synthesizing realistic 3D mesh deformation sequences is a challenging but important task in computer animation. To achieve this, researchers have long been focusing on shape analysis to develop new interpolation and extrapolation techniques. However, such techniques have limited learning capabilities and therefore often produce unrealistic deformation. Although there are already networks defined on individual meshes, deep architectures that operate directly on mesh sequences with temporal information remain unexplored due to the following major barriers: irregular mesh connectivity, rich temporal information, and varied deformation. To address these issues, we utilize convolutional neural networks defined on triangular meshes along with a shape deformation representation to extract useful features, followed by long short-term memory (LSTM) that iteratively processes the features. To fully respect the bidirectional nature of actions, we propose a new share-weight bidirectional scheme to better synthesize deformations. An extensive evaluation shows that our approach outperforms existing methods in sequence generation, both qualitatively and quantitatively.

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Section: Related Workmentioning

confidence: 99%

Synthesizing Mesh Deformation Sequences With Bidirectional LSTM

Qiao

Lai

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…The focus of this paper is on monocular vision techniques (i.e., approaches based only on 2D images as input data). Although deep learning models based on 3D or RGB-D images are becoming popular in visual recognition tasksand in some cases they provide better recognition performance than 2D approaches, especially in tasks such as depth perception, shape analysis and scene reconstruction [21]computational requirements and scarcity of training data limit their utility in applications such as robotics and visual SLAM. Moreover, the simplicity of 2D images make them more compelling for recognition tasks.…”

Section: Scope and Outline Of Surveymentioning

confidence: 99%

CNN Architectures for Geometric Transformation-Invariant Feature Representation in Computer Vision: A Review

Mumuni

2021

SN COMPUT. SCI.

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One of the main challenges in machine vision relates to the problem of obtaining robust representation of visual features that remain unaffected by geometric transformations. This challenge arises naturally in many practical machine vision tasks. For example, in mobile robot applications like simultaneous localization and mapping (SLAM) and visual tracking, object shapes change depending on their orientation in the 3D world, camera proximity, viewpoint, or perspective. In addition, natural phenomena such as occlusion, deformation, and clutter can cause geometric appearance changes of the underlying objects, leading to geometric transformations of the resulting images. Recently, deep learning techniques have proven very successful in visual recognition tasks but they typically perform poorly with small data or when deployed in environments that deviate from training conditions. While convolutional neural networks (CNNs) have inherent representation power that provides a high degree of invariance to geometric image transformations, they are unable to satisfactorily handle nontrivial transformations. In view of this limitation, several techniques have been devised to extend CNNs to handle these situations. This article reviews some of the most promising approaches to extend CNN architectures to handle nontrivial geometric transformations. Key strengths and weaknesses, as well as the application domains of the various approaches are also highlighted. The review shows that although an adequate model for generalized geometric transformations has not yet been formulated, several techniques exist for solving specific problems. Using these methods, it is possible to develop task-oriented solutions to deal with nontrivial transformations.

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“…Previously handcrafted feature descriptors usually cannot perform well on multiple datasets and tasks, and in recent years, state-of-the-art results have been achieved by data-driven automatic feature descriptor extraction methods. Xiao et al (2020) investigated these datadriven approaches and classified them into groups such as voxel-based, image-based, and surface-based. The image-based methods take depth images or a set of images as input and inevitably lose geometric details, while voxel-based methods that take a volumetric grid as input have high computation costs and cannot handle high-resolution data.…”

Section: Shape Representation Learningmentioning

confidence: 99%