IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment (2203.11590v1)

Abstract: This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) and further reason that temporal irregularity and under-sampling are two major challenges. To tackle the challenges, we propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. Specifically, we propose a temporal consistency learning module to align two consecutive point cloud frames point-wisely, based on which we can employ linear interpolation to obtain coarse trajectories/in-between frames. To compensate the high-order nonlinear components of trajectories, we apply aligned feature embeddings that encode local geometry properties to regress point-wise increments, which are combined with the coarse estimations. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually. Our framework can bring benefits to 3D motion data acquisition. The source code is publicly available at https://github.com/ZENGYIMING-EAMON/IDEA-Net.git.

• The paper introduces IDEA-Net, a deep learning framework that estimates point-wise trajectories to interpolate dynamic 3D point clouds with significant non-rigid deformations.
• IDEA-Net utilizes deep embeddings via dynamic graph CNNs and a temporal consistency module to estimate complex nonlinear trajectory components.
• IDEA-Net demonstrates state-of-the-art performance on diverse datasets, providing a practical tool for high temporal resolution 3D point cloud generation.

IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment

The paper "IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment" addresses the challenge of temporally interpolating dynamic 3D point clouds characterized by significant non-rigid deformations. Dynamic 3D point clouds, which are sequences of frames capturing spatial transformations over time, are fundamental in applications such as autonomous driving, virtual reality, and immersive communication. The difficulty in acquiring high temporal resolution (HTR) point clouds due to technological and economic constraints necessitates the development of computational methods to interpolate between low temporal resolution (LTR) frames.

IDEA-Net presents a novel deep learning framework to solve this problem, focusing on estimating point-wise trajectories. These trajectories, which represent smooth curves in 3D space, are essential for capturing the intricate movements and deformations of objects. The authors identify temporal irregularity and under-sampling as key challenges in this task, particularly when dealing with large non-rigid deformations.

The approach is structured into two main processes: coarse linear interpolation for initial trajectory estimation and a compensatory step for high-order nonlinear components. By embedding point clouds into high-dimensional feature spaces through the use of dynamic graph convolutional neural networks (DGCNN), IDEA-Net effectively captures both local and global geometric properties. The temporal consistency module within the network aligns consecutive frames to facilitate interpolation, with a relaxation mechanism to handle the binary nature of point alignment matrices.

The paper highlights the effectiveness of IDEA-Net with strong numerical results, demonstrating substantial performance improvements over state-of-the-art methods in both qualitative and quantitative evaluations. The authors conducted extensive experimentation using diverse datasets, showcasing the framework's robustness and generalization capabilities across different types of motion sequences.

Practically, IDEA-Net offers a valuable tool for enhancing HTR sequence generation in various domains, thus alleviating reliance on expensive sensor technologies. Theoretically, the approach enriches the field of 3D data processing by providing a structured method for interpolating complex motion sequences with an interpretable geometric foundation.

Looking forward, the research might focus on further optimizing the trajectory estimation mechanism, potentially integrating multi-frame sequence input or higher-order modeling to capture more complex motions and interactions. Moreover, there are prospects for extending the application to real-time processing in advanced VR systems or autonomous platforms, where immediate point cloud interpolation is critical.

In conclusion, the paper contributes a robust solution to a significant problem in 3D point cloud processing, with both immediate practical applications and a basis for future theoretical exploration in the domain of artificial intelligence and machine vision.