Neural ICP for Scalable and Robust 3D Human Registration

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NICP, a self-supervised task that iteratively refines neural field predictions to better align with the target surface, enables a scalable and robust 3D human registration pipeline (NSR) that generalizes across diverse data sources and challenges.

Samenvatting

The paper proposes a 3D human registration pipeline called Neural Scalable Registration (NSR) that combines a localized neural field (LoVD) with a novel self-supervised task called Neural ICP (NICP).
The key contributions are:

NICP: A self-supervised fine-tuning procedure that iteratively improves the geometric understanding of the neural field at inference time. NICP queries the neural field directly on the target surface points and uses the predicted offsets to update the network, promoting convergence towards the target.

NSR Pipeline: NSR combines NICP with LoVD, a localized variant of the Learned Vertex Descent (LVD) neural field, trained on a large MoCap dataset. This pipeline achieves state-of-the-art results on public benchmarks and can handle a wide range of real-world challenges like varied poses, identities, garments, noise, and partial data.

Code and Data Release: The authors provide the code and pre-trained network weights, enabling the research community to use a powerful tool for 3D human registration in diverse contexts.

The paper first provides background on point cloud registration and neural fields for this task. It then details the NICP self-supervised procedure and how it is integrated into the NSR pipeline. Extensive experiments demonstrate the effectiveness of NICP in improving the backbone neural field, as well as the generalization capabilities of the overall NSR method across various datasets and challenges.

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Belangrijkste Inzichten Gedestilleerd Uit

NICP

by Riccardo Mar... om arxiv.org 04-02-2024

https://arxiv.org/pdf/2312.14024.pdf

Diepere vragen

How could the NICP self-supervised task be extended to other 3D registration problems beyond human bodies?

The NICP self-supervised task can be extended to other 3D registration problems by adapting the iterative refinement process to suit the specific characteristics of different objects or shapes. One way to extend NICP is by customizing the correspondence and registration steps based on the unique features of the objects being registered. For example, for objects with complex geometries or articulated structures, the correspondence step could be modified to handle non-rigid deformations more effectively. Additionally, the registration step could be optimized to account for specific challenges such as occlusions, noise, or missing data in the input point clouds. By tailoring the NICP approach to the requirements of different 3D registration tasks, it can be applied to a wide range of objects beyond human bodies, such as vehicles, buildings, or natural landscapes.

What are the potential limitations of the neural field representation, and how could they be addressed to further improve the robustness of the registration?

One potential limitation of the neural field representation is its reliance on the training data distribution, which can lead to challenges in generalizing to out-of-distribution data. To address this limitation and improve the robustness of the registration, several strategies can be implemented. One approach is to augment the training data with a diverse set of shapes, poses, and environmental conditions to enhance the network's ability to handle variations in the input data. Additionally, incorporating regularization techniques or priors based on geometric constraints can help the neural field model to capture more accurate and reliable correspondences during registration. Furthermore, exploring ensemble methods or incorporating uncertainty estimation in the neural field predictions can enhance the model's robustness to noise and outliers in the input point clouds.

What other applications beyond registration could benefit from the scalable and generalizable 3D human modeling enabled by the NSR pipeline?

The scalable and generalizable 3D human modeling enabled by the NSR pipeline can benefit various applications beyond registration in the fields of computer vision, graphics, and virtual reality. Some potential applications include:

Virtual Try-On: NSR can be utilized for virtual try-on applications in the fashion industry, allowing users to visualize how clothing items fit and look on their 3D avatars accurately.

Animation and Gaming: The accurate 3D human modeling provided by NSR can enhance character animation in movies, video games, and virtual environments, enabling realistic movements and interactions.

Medical Imaging: NSR can be applied in medical imaging for patient-specific anatomical modeling, surgical planning, and simulation, improving the accuracy of procedures and treatments.

Biomechanics and Sports Science: NSR can aid in analyzing human movements, sports performance, and injury prevention by creating detailed 3D models for biomechanical simulations and analysis.

Augmented Reality: NSR can support augmented reality applications by enabling precise overlay of digital information on real-world environments, enhancing user experiences and interactions.

By leveraging the capabilities of the NSR pipeline for scalable and generalizable 3D human modeling, these applications can benefit from enhanced accuracy, realism, and efficiency in their respective domains.