PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views
Xin Fei, Wenzhao Zheng, Yueqi Duan, Wei Zhan, Masayoshi Tomizuka, Kurt, Keutzer, Jiwen Lu

TL;DR
PixelGaussian introduces a dynamic, view-adaptive 3D Gaussian reconstruction framework that improves efficiency and quality in multi-view 3D reconstruction tasks, outperforming existing methods.
Contribution
The paper presents PixelGaussian, a novel framework with a cascade Gaussian adapter and transformer-based refiner for adaptive, efficient 3D Gaussian reconstruction from arbitrary views.
Findings
Achieves state-of-the-art results on ACID and RealEstate10K datasets.
Effectively reduces Gaussian redundancy with increasing input views.
Demonstrates strong generalization across various view counts.
Abstract
We propose PixelGaussian, 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 PixelGaussian 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 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,…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Computer Graphics and Visualization Techniques · Advanced Vision and Imaging
MethodsSoftmax · Attention Is All You Need · Pruning · Adapter
