pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction (2312.12337v4)

Abstract: We introduce pixelSplat, a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fast 3D reconstruction at inference time. To overcome local minima inherent to sparse and locally supported representations, we predict a dense probability distribution over 3D and sample Gaussian means from that probability distribution. We make this sampling operation differentiable via a reparameterization trick, allowing us to back-propagate gradients through the Gaussian splatting representation. We benchmark our method on wide-baseline novel view synthesis on the real-world RealEstate10k and ACID datasets, where we outperform state-of-the-art light field transformers and accelerate rendering by 2.5 orders of magnitude while reconstructing an interpretable and editable 3D radiance field.

• The paper introduces pixelSplat, a novel approach that leverages 3D Gaussian splats for efficient, differentiable novel view synthesis.
• It employs a feed-forward reparameterization trick to propagate gradients, effectively overcoming local minima in sparse scene representations.
• PixelSplat achieves rendering speeds 2.5 orders of magnitude faster on RealEstate10k and ACID, unlocking real-time 3D scene applications.

Insightful Overview of "pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction"

The paper presents a novel approach, pixelSplat, for reconstructing 3D radiance fields using 3D Gaussian primitives derived from image pairs. This method aims to address significant challenges in scalable, generalizable novel view synthesis including computational efficiency and memory constraints, commonly encountered in differentiable rendering systems.

Methodological Contributions

pixelSplat employs a feed-forward model to predict a set of Gaussian primitives in 3D space. Each Gaussian primitive is parameterized by its mean, covariance, opacity, and spherical harmonics coefficients. The key innovation of this approach is its differentiable framework, made possible by a clever reparameterization trick. This trick involves sampling Gaussian means from a dense probability distribution, allowing the model to propagate gradients through the representation, thereby overcoming local minima issues inherent to traditional sparse representations.

Implementation and Evaluation

The authors harness this method to perform wide-baseline novel view synthesis, evaluating it on the RealEstate10k and ACID datasets. These datasets are real-world and feature wide baseline images, presenting a challenging test bed for novel view synthesis methods. Impressively, pixelSplat not only outperforms state-of-the-art light field transformers in this context but also achieves a rendering speed that is 2.5 orders of magnitude faster. These results demonstrate both the efficacy and efficiency of the pixelSplat approach.

Implications and Future Directions

From a theoretical standpoint, this research advances the understanding of primitive-based scene representations, contributing a novel method for overcoming local minima in such systems. Practically, the development of pixelSplat has significant implications for applications requiring real-time 3D scene editing and visualization, such as virtual reality and computer graphics, due to its explicit and interpretable representation.

The approach also opens possibilities for future exploration in generative modeling of 3D scenes, particularly in integrating this framework with stochastic processes like diffusion models. Furthermore, the differentiable nature of pixelSplat could make it applicable as a building block in larger end-to-end models across various domains requiring geometrically coherent image synthesis.

Conclusion

pixelSplat presents a significant step forward in the field of 3D reconstruction and rendering from image pairs by leveraging a novel representation that combines computational efficiency with interpretability. The strong numerical results and the framework's potential for scalability and extension highlight the relevance of this method in both academic research and industry applications. Future efforts could aim at incorporating this model into larger systems and exploring its applicability in different contexts of AI-driven scene synthesis.