Matrix3D: Large Photogrammetry Model All-in-One

• URL: http://arxiv.org/abs/2502.07685v1
• Date: Tue, 11 Feb 2025 16:36:55 GMT
• Title: Matrix3D: Large Photogrammetry Model All-in-One
• Authors: Yuanxun Lu, Jingyang Zhang, Tian Fang, Jean-Daniel Nahmias, Yanghai Tsin, Long Quan, Xun Cao, Yao Yao, Shiwei Li,
• Abstract summary: Matrix3D is a unified model that performs several photogrammetry subtasks. These include pose estimation, depth prediction, and view synthesis. It offers fine-grained control through multi-round interactions.
• Score: 31.034664725331073
• License:
• Abstract: We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model. Matrix3D utilizes a multi-modal diffusion transformer (DiT) to integrate transformations across several modalities, such as images, camera parameters, and depth maps. The key to Matrix3D's large-scale multi-modal training lies in the incorporation of a mask learning strategy. This enables full-modality model training even with partially complete data, such as bi-modality data of image-pose and image-depth pairs, thus significantly increases the pool of available training data. Matrix3D demonstrates state-of-the-art performance in pose estimation and novel view synthesis tasks. Additionally, it offers fine-grained control through multi-round interactions, making it an innovative tool for 3D content creation. Project page: https://nju-3dv.github.io/projects/matrix3d.

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