3D Congealing: 3D-Aware Image Alignment in the Wild (2404.02125v1)

Abstract: We propose 3D Congealing, a novel problem of 3D-aware alignment for 2D images capturing semantically similar objects. Given a collection of unlabeled Internet images, our goal is to associate the shared semantic parts from the inputs and aggregate the knowledge from 2D images to a shared 3D canonical space. We introduce a general framework that tackles the task without assuming shape templates, poses, or any camera parameters. At its core is a canonical 3D representation that encapsulates geometric and semantic information. The framework optimizes for the canonical representation together with the pose for each input image, and a per-image coordinate map that warps 2D pixel coordinates to the 3D canonical frame to account for the shape matching. The optimization procedure fuses prior knowledge from a pre-trained image generative model and semantic information from input images. The former provides strong knowledge guidance for this under-constraint task, while the latter provides the necessary information to mitigate the training data bias from the pre-trained model. Our framework can be used for various tasks such as correspondence matching, pose estimation, and image editing, achieving strong results on real-world image datasets under challenging illumination conditions and on in-the-wild online image collections.

• The paper introduces a novel 3D congealing framework that jointly optimizes a canonical 3D space and individual image poses without relying on preset shape templates.
• It leverages pre-trained diffusion models and deep semantic features from models like DINO to robustly align semantically similar 2D images amid diverse appearances.
• Empirical evaluations show improved pose estimation performance with a mean rotation error of 26.97° and superior results compared to baseline methods.

Overview of "3D Congealing: 3D-Aware Image Alignment in the Wild"

The paper introduces a novel computational framework termed "3D Congealing," designed to address the complex task of aligning semantically similar objects within two-dimensional (2D) images into a unified three-dimensional (3D) space. This methodology eschews traditional dependencies on pre-defined shape templates, camera parameters, and poses, thereby enabling versatile applications such as pose estimation and image editing.

The proposed framework leverages a canonical 3D representation that effectively encapsulates geometric and semantic details. The method aggregates information from unannotated 2D Internet images, congealing this data into a coherent 3D canonical space. This space serves not only as a unifying structure for various 2D image inputs but is also crucial in facilitating the alignment process.

Methodological Innovation

At the heart of the proposed solution is a dual optimization procedure. This process concurrently refines the canonical 3D space and estimates poses for individual image inputs. The authors employ a two-pronged approach: using a combination of pre-trained generative models and semantic-rich features extracted from input images. The generative models contribute crucial prior knowledge to guide the solution search, while the deep semantic features offset the biases inherent in the generative models, ensuring the procedure remains grounded in the actual input data.

1. Generative Model Guidance: The authors utilize pre-trained text-to-image diffusion models to obtain initial plausible 3D shapes. A noteworthy innovation is the implementation of Textual Inversion, which optimizes textual embeddings to best reflect the input images within the generative model framework.
2. Semantic Consistency via Deep Features: To integrate semantic understanding, the framework utilizes features from advanced pre-trained models such as DINO, enhancing the ability to establish robust 2D-3D correspondences. This ensures that the framework can adeptly handle object instances with significant variations in shape and texture.
3. Optimization Strategy: The approach optimizes for both the canonical shape and camera poses, uniquely leveraging joint optimization protocols that emphasize semantic image consistency and IoU loss-based shape grounding.

Empirical Evaluation

The effectiveness of the 3D Congealing framework is demonstrated through experiments on image datasets capturing real-world scenarios with significant illumination and background noise variance. Notably, the framework surpasses baseline strategies in pose estimation tasks within the multi-illumination context of the NAVI dataset, even outperforming methods like SAMURAI that assume initial pose knowledge. The results reveal a mean rotation error of approximately 26.97 degrees, marking a significant improvement in geometric alignment processes.

Applications and Implications

While primarily focused on pose estimation, the implications of 3D Congealing extend to various domains, including image-based 3D reconstruction, semantic editing, and alignment tasks for web-acquired images. The ability to derive a canonical 3D representation from diverse and unannotated image sources opens up possibilities for constructing 3D models from everyday Internet searches or personal photo collections without extensive setup or data preprocessing.

Theoretically, this work elevates the discussion around multimodal alignment in computer vision, especially in exploring integration prospects between generative and discriminative model paradigms. Looking forward, the framework presents potential for enhancement with more adaptive models that could dynamically adjust their strategies based on the variance present in the input data.

In conclusion, the 3D Congealing framework is a robust, conceptually innovative contribution to the field of computer vision, carving a path for future developments in the practically challenging yet theoretically enriching domain of 3D-aware image processing. This work not only provides methodological advances but also paves the way for broader, application-driven exploration in automated 3D modeling from 2D visual data.