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Computer Science > Computer Vision and Pattern Recognition

arXiv:2407.08447 (cs)
[Submitted on 11 Jul 2024]

Title:WildGaussians: 3D Gaussian Splatting in the Wild

Authors:Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys
, Torsten Sattler
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in the Wild, by Jonas Kulhanek and 4 other authors
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    Abstract:While the field of 3D scene reconstruction is dominated
    by NeRFs due to their photorealistic quality, 3D Gaussian
    Splatting (3DGS) has recently emerged, offering similar quality
    with real-time rendering speeds. However, both methods primarily
    excel with well-controlled 3D scenes, while in-the-wild data -
    characterized by occlusions, dynamic objects, and varying
    illumination - remains challenging. NeRFs can adapt to such
    conditions easily through per-image embedding vectors, but 3DGS
    struggles due to its explicit representation and lack of shared
    parameters. To address this, we introduce WildGaussians, a novel
    approach to handle occlusions and appearance changes with 3DGS.
    By leveraging robust DINO features and integrating an appearance
    modeling module within 3DGS, our method achieves state-of-the-art
    results. We demonstrate that WildGaussians matches the real-time
    rendering speed of 3DGS while surpassing both 3DGS and NeRF
    baselines in handling in-the-wild data, all within a simple
    architectural framework.

Comments: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2407.08447 [cs.CV]
          (or arXiv:2407.08447v1 [cs.CV] for this version)

Submission history

From: Jonas Kulhanek [view email]
[v1] Thu, 11 Jul 2024 12:41:32 UTC (28,709 KB)
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