3D Congealing: Aligning Semantically Similar Objects in 3D from Unconstrained Image Collections

Основні поняття

Given a collection of unlabeled images capturing semantically similar objects, the goal is to infer a canonical 3D representation that encapsulates the shared geometric and semantic information, and to establish dense 2D-3D correspondences between the input images and the canonical 3D space.

Анотація

The proposed framework for 3D Congealing tackles the challenge of aligning images of semantically similar objects without assuming any prior knowledge about the object shapes, poses, or camera parameters.
The key components are:

Generative Model Guidance:

The framework utilizes a pre-trained text-to-image diffusion model to obtain a plausible 3D shape prior that is compliant with the input image observations.
It optimizes for the most suitable text embedding to reconstruct the input images via the pre-trained model.

Semantic Consistency:

The method uses pre-trained deep semantic features, such as DINO, to define a distance metric that aligns input images in the semantic space, tolerating variations in object shape, texture, and illumination.
It learns canonical coordinate mappings that establish dense 2D-3D correspondences between the input images and the canonical 3D representation.

Optimization:

The framework jointly optimizes for the canonical 3D representation, the pose of each input image, and the canonical coordinate mappings.
It fuses the prior knowledge from the generative model and the semantic information from the input images to tackle this under-constrained task.

The proposed method demonstrates strong performance on pose estimation tasks with in-the-wild image captures under varying illumination conditions, and enables various applications such as image editing by leveraging the obtained 2D-3D correspondences.

Статистика

The method is evaluated on the NAVI dataset, which contains 35 object image collections with ground truth camera poses.

Цитати

"We propose the task of 3D Congealing, where the goal is to align a collection of images containing semantically similar objects into a shared 3D space."
"Obtaining a canonical 3D representation and grounding input images to the 3D canonical space enable several downstream tasks, such as 6-DoF object pose estimation, pose-aware image filtering, and image editing."

Ключові висновки, отримані з

3D Congealing

by Yunzhi Zhang... о arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.02125.pdf

Глибші Запити

How can the proposed framework be extended to handle more diverse object categories, such as articulated or deformable shapes, beyond the rigid objects considered in this work

To extend the proposed framework to handle more diverse object categories, such as articulated or deformable shapes, beyond the rigid objects considered in this work, several modifications and enhancements can be implemented:

Deformable Shape Representation: Introduce a more flexible representation for deformable shapes, such as articulated objects or objects with varying poses. This could involve incorporating deformable models like deformable neural radiance fields or mesh-based representations that can capture the variability in shape.

Dynamic Pose Estimation: Implement a dynamic pose estimation module that can handle articulated objects by predicting joint angles or keypoint locations. This would enable the framework to align images of objects with complex deformations or movements.

Temporal Consistency: Incorporate temporal information to track deformable objects across frames. By considering the temporal evolution of object shapes, the framework can better handle deformable objects in motion.

Semantic Segmentation: Enhance the semantic understanding of objects by incorporating semantic segmentation networks. This can help in identifying different parts of deformable objects and aligning them accurately in the 3D space.

By integrating these enhancements, the framework can effectively handle a wider range of object categories, including articulated and deformable shapes, enabling more versatile applications in various domains.

What are the potential limitations of relying on pre-trained semantic feature extractors, and how could the framework be made more robust to feature ambiguities

Relying solely on pre-trained semantic feature extractors may have limitations, especially when dealing with feature ambiguities in complex scenes. To make the framework more robust to feature ambiguities, the following strategies can be implemented:

Multi-Modal Features: Incorporate multi-modal features, including depth information or optical flow, to complement the visual features extracted by pre-trained models. This can provide additional cues for disambiguating features in challenging scenarios.

Adaptive Feature Fusion: Implement adaptive feature fusion techniques that dynamically adjust the importance of different features based on the context of the scene. This can help in prioritizing more reliable features in ambiguous regions.

Self-Supervised Learning: Introduce self-supervised learning methods to learn feature representations directly from the data. By training the model to predict certain properties of the scene, it can learn more robust and discriminative features that are less prone to ambiguities.

Attention Mechanisms: Utilize attention mechanisms to focus on relevant parts of the input images and suppress noise or irrelevant information. This can help in improving the robustness of feature extraction in the presence of ambiguities.

By incorporating these strategies, the framework can enhance its ability to handle feature ambiguities and improve the accuracy of semantic feature extraction in challenging scenarios.

Given the ability to align images in 3D, how could this capability be leveraged to enable novel applications in areas like augmented reality, robotics, or computational photography

The capability to align images in 3D opens up a wide range of novel applications in various fields:

Augmented Reality (AR): The 3D alignment capability can be leveraged in AR applications to accurately overlay virtual objects onto the real world. By aligning virtual objects with the physical environment in real-time, AR experiences can be more immersive and interactive.

Robotics: In robotics, 3D image alignment can be used for object recognition, manipulation, and navigation tasks. Robots can use the aligned 3D information to understand the spatial relationships between objects and interact with the environment more effectively.

Computational Photography: In computational photography, 3D alignment can enhance image editing and post-processing techniques. By aligning images in 3D space, photographers can apply more precise edits, such as relighting scenes, changing perspectives, or creating depth-of-field effects.

Medical Imaging: In the field of medical imaging, 3D alignment can aid in the analysis of volumetric data, such as CT scans or MRI images. By aligning medical images in 3D, doctors and researchers can better visualize and interpret complex anatomical structures.

Overall, the 3D alignment capability has the potential to revolutionize various applications by providing a deeper understanding of spatial relationships and enabling more advanced functionalities in AR, robotics, computational photography, and medical imaging.