Alapfogalmak
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.
Kivonat
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.