Neural Semantic Surface Maps: Automated Semantic Correspondences Extraction

Основні поняття

The authors propose an automated method to compute semantic correspondences between 3D surfaces using pre-trained vision models, eliminating the need for manual annotations or training data.

Анотація

The content introduces a method for computing semantic correspondences between 3D surfaces automatically. By distilling fuzzy matches from pre-trained vision models and optimizing inter-surface maps, the approach eliminates the need for manual annotations or training data. The method shows promise in generating accurate and semantically valid maps for both isometrically and non-isometrically related shape pairs.

Статистика

Lack of annotated data prohibits direct inference of 3D semantic priors.
Our method renders untextured 3D shapes from multiple viewpoints.
We distill semantic matches from pre-trained vision models.
The resulting renders are fed into an off-the-shelf image-matching strategy.
Neural Surface Maps (NSM) framework encodes surfaces using neural functions.
NSM has two limitations: exact boundary correspondences and landmark correspondences requirement.
Seamless Neural Surface Maps (sNSM) framework relaxes boundary correspondence requirements.
Fuzzy matches are extracted from renderings using DinoV2 features.
Aggregating fuzzy matches to produce an inter-surface map is optimized with a custom objective scheme.
Total loss includes terms for matching accuracy, bijectivity, seamlessness, and smoothness.

Цитати

"Our approach exploits a similar intuition to extract semantic shape correspondences, distilling an inter-surface map from them."
"We illustrate that our approach can generate semantic surface-to-surface maps, eliminating manual annotations or any 3D training data requirement."

Ключові висновки, отримані з

Neural Semantic Surface Maps

by Luca Morreal... о arxiv.org 03-11-2024

https://arxiv.org/pdf/2309.04836.pdf

Глибші Запити

How can this automated method be applied to other domains beyond 3D shapes

This automated method for computing semantic surface-to-surface maps can be applied to various other domains beyond 3D shapes. For example, it could be utilized in medical imaging for aligning and mapping anatomical structures or organs from different scans. This could aid in tasks such as comparing MRI images over time or across patients. In robotics, the method could help map and align different robot parts or components for assembly tasks. Additionally, in computer vision applications like object recognition and tracking, this approach could assist in matching objects with varying poses or appearances.

What potential challenges could arise when dealing with highly complex shapes

Dealing with highly complex shapes can present several challenges when using this automated method. One challenge is handling intricate geometries that may have overlapping or intersecting parts, leading to difficulties in accurately extracting semantic correspondences. Another challenge is ensuring robustness against occlusions where parts of the shape are hidden from view, impacting the accuracy of extracted matches. Moreover, thin structures within shapes might pose a challenge as they require precise parameterization and mapping which can be challenging due to their limited geometry.

How might advancements in neural networks impact the accuracy and efficiency of this method in the future

Advancements in neural networks can significantly impact the accuracy and efficiency of this method in the future. Improved network architectures with better feature extraction capabilities can enhance the quality of semantic correspondences extracted from visual data like renderings. Enhanced training techniques such as meta-learning or self-supervised learning can lead to more robust models that generalize well across different datasets and shapes. Furthermore, advancements in computational hardware like GPUs can speed up optimization processes involved in generating inter-surface maps, making the method more efficient for real-time applications.