PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views
Keywords: 3D Gaussian splatting, 3D reconstruction, Novel view synthesis
Abstract: We propose AdaptiveGaussian, an efficient feed-forward framework for learning generalizable 3D Gaussian reconstruction from arbitrary views. Most existing methods rely on uniform pixel-wise Gaussian representations, which learn a fixed number of 3D Gaussians for each view and cannot generalize well to more input views. Differently, our AdaptiveGaussian dynamically adapts both the Gaussian distribution and quantity based on geometric complexity, leading to more efficient representations and significant improvements in reconstruction quality. Specifically, we introduce a Cascade Gaussian Adapter (CGA) to adjust Gaussian distribution according to local geometry complexity identified by a keypoint scorer. CGA leverages deformable attention in context-aware hypernetworks to guide Gaussian pruning and splitting, ensuring accurate representation in complex regions while reducing redundancy. Furthermore, we design a transformer-based Iterative Gaussian Refiner (IGR) module that refines Gaussian representations through direct image-Gaussian interactions. Our AdaptiveGaussian can effectively reduce Gaussian redundancy as input views increase. We conduct extensive experiments on the large-scale ACID and RealEstate10K datasets, where our method achieves state-of-the-art performance with good generalization to various numbers of views.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 2562
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