VicaSplat: A Single Run is All You Need for 3D Gaussian Splatting and Camera Estimation from Unposed Video Frames

• URL: http://arxiv.org/abs/2503.10286v1
• Date: Thu, 13 Mar 2025 11:56:05 GMT
• Title: VicaSplat: A Single Run is All You Need for 3D Gaussian Splatting and Camera Estimation from Unposed Video Frames
• Authors: Zhiqi Li, Chengrui Dong, Yiming Chen, Zhangchi Huang, Peidong Liu,
• Abstract summary: We present VicaSplat, a novel framework for joint 3D Gaussians reconstruction and camera pose estimation.<n>The core of our method lies in a novel transformer-based network architecture.
• Score: 8.746291192336056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present VicaSplat, a novel framework for joint 3D Gaussians reconstruction and camera pose estimation from a sequence of unposed video frames, which is a critical yet underexplored task in real-world 3D applications. The core of our method lies in a novel transformer-based network architecture. In particular, our model starts with an image encoder that maps each image to a list of visual tokens. All visual tokens are concatenated with additional inserted learnable camera tokens. The obtained tokens then fully communicate with each other within a tailored transformer decoder. The camera tokens causally aggregate features from visual tokens of different views, and further modulate them frame-wisely to inject view-dependent features. 3D Gaussian splats and camera pose parameters can then be estimated via different prediction heads. Experiments show that VicaSplat surpasses baseline methods for multi-view inputs, and achieves comparable performance to prior two-view approaches. Remarkably, VicaSplat also demonstrates exceptional cross-dataset generalization capability on the ScanNet benchmark, achieving superior performance without any fine-tuning. Project page: https://lizhiqi49.github.io/VicaSplat.

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