SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting (2411.17190v5)

Abstract: We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These settings are inherently ill-posed due to the lack of ground-truth data, learned geometric information, and the need to achieve accurate 3D reconstruction without finetuning, making it difficult for conventional methods to achieve high-quality results. Our model addresses these challenges by effectively integrating explicit 3D representations with self-supervised depth and pose estimation techniques, resulting in reciprocal improvements in both pose accuracy and 3D reconstruction quality. Furthermore, we incorporate a matching-aware pose estimation network and a depth refinement module to enhance geometry consistency across views, ensuring more accurate and stable 3D reconstructions. To present the performance of our method, we evaluated it on large-scale real-world datasets, including RealEstate10K, ACID, and DL3DV. SelfSplat achieves superior results over previous state-of-the-art methods in both appearance and geometry quality, also demonstrates strong cross-dataset generalization capabilities. Extensive ablation studies and analysis also validate the effectiveness of our proposed methods. Code and pretrained models are available at https://gynjn.github.io/selfsplat/

• The paper introduces SelfSplat, which forgoes pose data and 3D priors by leveraging self-supervised depth and pose estimation with 3D Gaussian Splatting.
• It employs a matching-aware pose network and a depth refinement module to enhance multi-view consistency and geometric accuracy.
• Evaluations demonstrate superior geometric and appearance quality across diverse datasets, underscoring its robust cross-dataset generalization.

Analysis of SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting

The paper "SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting" introduces a novel approach to 3D reconstruction that leverages the principles of 3D Gaussian Splatting without dependency on pose information or pre-existing 3D priors. This research tackles significant challenges in the domain of computer vision, specifically addressing the limitations apparent in methods that require substantial computational resources and dataset-specific fine-tuning.

Summary

The authors present a robust framework, SelfSplat, designed for the pose-free, generalizable synthesis of 3D scenes from unposed multi-view images. By integrating 3D Gaussian Splatting with self-supervised depth and pose estimation, SelfSplat steps beyond conventional methods reliant on significant geometric information and extensive training on dataset-specific poses. It incorporates a matching-aware pose estimation network and a depth refinement module, aiming to enhance multi-view consistency in geometry, thereby achieving more accurate and stable 3D reconstructions.

Three datasets—RealEstate10K, ACID, and DL3DV—serve as the evaluation benchmarks for SelfSplat. The results establish that SelfSplat achieves superior geometric and appearance quality over previous state-of-the-art methods. Furthermore, the model demonstrates noteworthy cross-dataset generalization, presenting a significant advantage in scalability and broader applicability.

Technical Insights

SelfSplat's key innovation lies in its self-supervised learning mechanism, which autonomously predicts depth, camera poses, and Gaussian attributes using a unified neural network architecture. It capitalizes on self-supervised depth and pose estimation techniques to substitute for the direct use of ground-truth pose data, utilizing photometric consistency across views. This method circumvents the intrinsic limitations of methods like NeRF, which impose significant computational burdens due to volumetric rendering demands.

Central to this approach is the integration of a matching-aware pose network, which utilizes cross-view features to bolster pose estimation accuracy. This network improves the geometric consistency and corrects pose estimation errors that can compromise scene coherence. Additionally, the depth refinement module leverages estimated poses as embedding features, further enhancing the robustness and accuracy of the 3D geometry reconstructions.

Implications

The implications of this research are substantial for the fields of augmented reality, virtual reality, and robotics, where efficient and scalable 3D reconstruction is vital. The ability of SelfSplat to generalize across datasets without the necessity for scene-specific tuning indicates promising utilization in less controlled, "in-the-wild" data scenarios prevalent in real-world applications.

Future Directions

Advancements in this area could be directed towards expanding SelfSplat's capabilities in handling more extensive baseline scenarios, including complete 360-degree reconstructions. Additionally, extending its robustness to dynamic environments with moving objects, as well as integrating multi-modal priors, could enhance its applicability in more diverse and complex scenes. Addressing these areas would solidify SelfSplat's role in improving generalizable 3D scene reconstruction.

Conclusion

SelfSplat represents a significant stride towards efficient and generalizable 3D reconstruction technologies. By eliminating dependencies on pre-established 3D priors and pose data, it enables broader applicability and resource-efficient implementations. This work contributes positively to the expansion and accessibility of large-scale 3D reconstruction solutions, setting a new trajectory for subsequent developments in the field.