Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction (2012.01451v1)

Abstract: We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation graph via a deep neural network. This neural deformation graph does not rely on any object-specific structure and, thus, can be applied to general non-rigid deformation tracking. Our method globally optimizes this neural graph on a given sequence of depth camera observations of a non-rigidly moving object. Based on explicit viewpoint consistency as well as inter-frame graph and surface consistency constraints, the underlying network is trained in a self-supervised fashion. We additionally optimize for the geometry of the object with an implicit deformable multi-MLP shape representation. Our approach does not assume sequential input data, thus enabling robust tracking of fast motions or even temporally disconnected recordings. Our experiments demonstrate that our Neural Deformation Graphs outperform state-of-the-art non-rigid reconstruction approaches both qualitatively and quantitatively, with 64% improved reconstruction and 62% improved deformation tracking performance.

• The paper introduces Neural Deformation Graphs that use a neural network to learn deformation models without relying on domain-specific priors.
• It employs global optimization with viewpoint and inter-frame consistency constraints, achieving up to 64% improvement in reconstruction accuracy.
• The method enhances real-time tracking for augmented reality and robotics by accurately capturing complex, non-rigid deformations across diverse scenarios.

Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction

The paper "Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction" proposes a novel approach for the tracking and 3D reconstruction of non-rigidly deforming objects using neural networks. This work addresses the challenge of consistently and accurately capturing dynamic scenes, which has been an impediment in the field of computer vision and graphics, especially in scenarios involving fast motions or temporally disconnected recordings.

Overview of the Method

The authors introduce Neural Deformation Graphs (NDG), a deep learning model that implicitly predicts a deformation graph for non-rigid objects from sequence data obtained through depth cameras. The core concept is to represent the deformation graph using a neural network that can globally optimize the non-rigid reconstruction process. The method does not depend on object-specific structures or motion priors, making it applicable to a wide array of deformable objects. This generality is achieved through several innovative strategies:

1. Implicit Deformation Graph Modeling: The deformation graph is learned via a neural network, eliminating the need for domain-specific priors. This network estimates graph nodes that contain positional and rotational information, aiding in consistent tracking across various frames.
2. Global Optimization: The framework employs a global optimization approach using several constraints like viewpoint consistency and inter-frame graph and surface consistency. These ensure that the model captures the object's deformation comprehensively across multiple inputs and viewpoints.
3. Implicit Shape Representation: An implicit multi-MLP representation anchors the geometry on the scene-specific deformation graph nodes. This allows capturing fine details without an explicit canonical pose, valuable for handling diverse non-rigid configurations.

Numerical Results and Claims

Numerical results within the paper indicate a significant improvement over existing non-rigid reconstruction methods. Specifically, the NDG improves reconstruction accuracy by 64% and deformation tracking by 62%, compared to state-of-the-art approaches. These findings are supported by extensive evaluations using both synthetic and real-world data. The synthetic data experiments demonstrate that NDG provides more accurate geometry and deformation tracking than existing methods like DynamicFusion, SIF, and LDIF, under varied conditions including complex human-like motions and animal figures.

Implications

The primary practical implication of this research is the robust framework it provides for dynamic scene reconstruction using commodity depth sensors. This capability is especially relevant for applications in augmented reality, virtual reality, and robotics where real-time processing and accurate tracking of non-rigid objects are crucial. Theoretically, the introduction of neural deformation graphs pioneers a method for implicit graph learning in dynamic environments, pushing forward the understanding and development of deep learning models in 3D vision tasks.

Speculation on Future Developments

In the context of AI and non-rigid motion reconstruction, this work opens avenues for several future research directions. The authors suggest that the global optimization methodology could be used to infer data-driven priors, enhancing the model's capability to learn from large-scale datasets. Furthermore, adapting the approach to incorporate high-definition texture reconstruction alongside geometry could improve realism in applications where intricate surface details are critical.

Given these developments, the model's potential scalability and adaptation to higher resolution grids using sparse 3D convolutions are promising for more detailed and comprehensive real-world usage. We can anticipate the integration of such robust non-rigid tracking models with larger multi-sensor systems, extending its applicability to complex live settings.

In conclusion, the paper presents a substantial contribution to the field of non-rigid 3D reconstruction through neural networks, setting the stage for enhanced methodologies in capturing dynamically deforming objects accurately and consistently across diverse scenarios.