6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model (2407.15484v1)

Abstract: We propose 6DGS to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. 6DGS avoids the iterative process typical of analysis-by-synthesis methods (e.g. iNeRF) that also require an initialization of the camera pose in order to converge. Instead, our method estimates a 6DoF pose by inverting the 3DGS rendering process. Starting from the object surface, we define a radiant Ellicell that uniformly generates rays departing from each ellipsoid that parameterize the 3DGS model. Each Ellicell ray is associated with the rendering parameters of each ellipsoid, which in turn is used to obtain the best bindings between the target image pixels and the cast rays. These pixel-ray bindings are then ranked to select the best scoring bundle of rays, which their intersection provides the camera center and, in turn, the camera rotation. The proposed solution obviates the necessity of an "a priori" pose for initialization, and it solves 6DoF pose estimation in closed form, without the need for iterations. Moreover, compared to the existing Novel View Synthesis (NVS) baselines for pose estimation, 6DGS can improve the overall average rotational accuracy by 12% and translation accuracy by 22% on real scenes, despite not requiring any initialization pose. At the same time, our method operates near real-time, reaching 15fps on consumer hardware.

• The paper presents a non-iterative method using 3D Gaussian Splatting to estimate 6D camera poses without iterative refinement.
• It employs an attention-based pixel-ray binding mechanism and weighted least squares to accurately compute both translation and rotation.
• Experimental results demonstrate a 12% improvement in rotation and 22% in translation accuracy, achieving near real-time performance at 15 fps.

6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model

The paper "6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model" introduces a novel approach to estimate the camera pose using a 3D Gaussian Splatting (3DGS) model. The authors tackle the challenge of 6DoF pose estimation without requiring iterative optimization, representing a significant advancement from conventional analysis-by-synthesis methods like iNeRF. This summary will explore the methodology, experimental results, and implications while speculating on future developments in AI, specifically in the domain of pose estimation.

Methodology

The proposed 6DGS method leverages a 3DGS model to estimate the camera pose of an input RGB image without iterative processes, making it distinct from conventional approaches such as iNeRF and NeMo+VoGE. The key innovation lies in reversing the 3DGS rendering process to infer camera poses effectively.

Radiant Ellicell and Ray Casting

One of the central components of this work is the concept of the radiant Ellicell, which facilitates uniform ray casting from the ellipsoidal surface of the 3D Gaussian splats. Each Ellicell projects rays uniformly distributed from the surface of the ellipsoids, leveraging the photometric parameters of the rendering model.

Attention-Based Pixel-Ray Binding

The authors propose an attention-based mechanism to bind the rays cast from the ellipsoids to the image pixels efficiently. This attention map, based on DINOv2, scores each pixel-ray binding, ultimately assisting in selecting the optimal ray bundle for accurate pose estimation. The selected bundle of rays intersects at the optical center of the camera, forming the basis for estimating the translation and rotation of the camera in a closed-form solution, bypassing the need for iterative refinement.

Weighted Least Squares for Pose Estimation

After selecting the top candidate rays based on their binding scores, the poses are derived using a weighted Least Squares method. The weights correspond to the attention scores, providing an efficient and accurate estimation of the camera's optical center and thus determining the camera's position and orientation.

Experimental Results

The proposed method was evaluated on the Tanks and Temples and Mip-NeRF 360\textdegree datasets, illustrating significant advancements over baseline methods. Experimental results indicated an improvement in rotational accuracy by 12% and translational accuracy by 22% compared to state-of-the-art methods. Additionally, 6DGS exhibited near real-time performance, achieving 15 fps on consumer hardware. These results underscore the efficacy of the approach in both accuracy and speed, emphasizing its potential for practical applications.

Implications and Future Developments

Practical Implications

1. Real-Time Applications: The real-time capability of 6DGS makes it highly suitable for applications such as robotics, AR/VR, and autonomous navigation, where fast and accurate pose estimation is critical.
2. Robustness: The method's performance without requiring an initial pose hint underscores its robustness, making it applicable in diverse and dynamic environments where initialization may not be feasible.

Theoretical Implications

1. Ellicell and Ray Casting: The introduction of the radiant Ellicell for uniform ray casting from ellipsoids opens new avenues for non-iterative rendering approaches in 3D modeling and pose estimation.
2. Attention Mechanisms: The utilization of attention maps for binding rays to image pixels highlights the potential of leveraging learning-based approaches to enhance geometric and photometric consistency.

Speculation on Future Developments

1. Integration with Deep Learning: Future research could focus on integrating more sophisticated deep learning models to mitigate noise and improve the accuracy of the attention-based binding mechanism.
2. Meta-Learning for Generalization: The approach may benefit from meta-learning frameworks to generalize across various scenes without retraining, enhancing its practicality for real-world deployment.
3. Scalability: Exploring the scalability of 6DGS to handle larger and more complex scenes with high fidelity could be a promising direction, potentially extending its applicability to urban-scale environments.

In summary, the 6DGS method presents a significant step forward in 6DoF pose estimation by circumventing iterative processes and providing high accuracy and real-time performance. Its deployment across various applications and further integration with advanced AI techniques holds substantial promise for the future of computer vision and related fields.