LEAP: Liberate Sparse-view 3D Modeling from Camera Poses (2310.01410v1)

Abstract: Are camera poses necessary for multi-view 3D modeling? Existing approaches predominantly assume access to accurate camera poses. While this assumption might hold for dense views, accurately estimating camera poses for sparse views is often elusive. Our analysis reveals that noisy estimated poses lead to degraded performance for existing sparse-view 3D modeling methods. To address this issue, we present LEAP, a novel pose-free approach, therefore challenging the prevailing notion that camera poses are indispensable. LEAP discards pose-based operations and learns geometric knowledge from data. LEAP is equipped with a neural volume, which is shared across scenes and is parameterized to encode geometry and texture priors. For each incoming scene, we update the neural volume by aggregating 2D image features in a feature-similarity-driven manner. The updated neural volume is decoded into the radiance field, enabling novel view synthesis from any viewpoint. On both object-centric and scene-level datasets, we show that LEAP significantly outperforms prior methods when they employ predicted poses from state-of-the-art pose estimators. Notably, LEAP performs on par with prior approaches that use ground-truth poses while running $400\times$ faster than PixelNeRF. We show LEAP generalizes to novel object categories and scenes, and learns knowledge closely resembles epipolar geometry. Project page: https://hwjiang1510.github.io/LEAP/

• The paper introduces a pose-free approach that bypasses traditional camera pose dependency in sparse-view 3D modeling.
• It employs a shared neural volume to directly encode geometric and texture data, ensuring robust feature aggregation across views.
• Extensive evaluations show the method outperforms pose-based models and matches ground-truth pose accuracy while operating 400x faster.

Insights into Pose-Free Sparse-View 3D Modeling with

The discussed research addresses a significant challenge in sparse-view 3D modeling: the dependence on camera poses. Traditional methods predominantly rely on accurately estimated camera poses to map 2D images onto a 3D model. However, obtaining precise camera poses, especially under sparse-view conditions, poses a considerable challenge, often resulting in degraded modeling performance. This paper introduces , a novel framework that forgoes the necessity of camera poses by employing a pose-free approach, effectively challenging the entrenched belief that camera poses are essential for multi-view 3D modeling.

Utilizing a neural volume that is shared across scenes, encodes geometric and textural information directly from the data, mitigating the need for pose information. The neural volume facilitates the projection of 2D image features into 3D, thus constructing a neural radiance field capable of synthesizing novel views. By leveraging feature-similarity-driven aggregation methods instead of traditional pose-based operations, updates the neural volume iteratively to capture long-range dependencies and geometric correlations across views. This innovative paradigm eliminates the error-prone nature of pose estimation, offering robustness against inaccuracies typically encountered with sparse-view inputs.

The evaluation conducted is extensive, encompassing a range of datasets including object-centric datasets such as OmniObject3D, Kubric-ShapeNet, and Objaverse, alongside scene-level datasets like DTU. The results indicate that not only outperforms prior models using predicted camera poses but also achieves comparable performance with those using ground-truth poses, while remarkably operating $400\times$ faster than PixelNeRF. This efficient performance highlights the effectiveness of in accurately reconstructing scenes and objects without explicit dependency on camera alignment.

The implications of this research are manifold. Practically, it opens a path to more resilient 3D modeling systems that can function reliably in environments where camera pose data is inaccurate or entirely unavailable. This adaptability is particularly beneficial for applications such as online retail, where product images feature varied, sparse viewpoints. Theoretically, this approach might inspire new methodologies of utilizing data-driven geometric priors, possibly informing future developments in volumetric neural rendering and potentially contributing insights into more abstract challenges in computer vision such as unsupervised learning.

Beyond its immediate contributions, this research prompts further investigation into enhancing the robustness and generalization ability of pose-free systems across even broader categories of objects and scenes. Future work could explore refining the neural volume design to account for larger, potentially unbounded scenes, or the integration with partial pose data where available, to refine and enhance the neural volume's learning capacity. Additionally, as demonstrated in the pre-training analysis, pre-training on extensive datasets appears promising for bolstering performance on novel scenarios, indicating a direction resembling foundational models in vision tasks.

In sum, provides a substantial leap forward in adapting 3D modeling techniques to a paradigm less reliant on external pose estimation, asserting a significant step towards more adaptable and general-purpose neural modeling frameworks.