MC-NeRF: Muti-Camera Neural Radiance Fields for Muti-Camera Image Acquisition Systems
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Neural Radiance Fields (NeRF) employ multi-view images for 3D scene representation and have shown remarkable performance. As one of the primary sources of multi-view images, multi-camera systems encounter challenges such as varying intrinsic parameters and frequent pose changes. Most previous NeRF-based methods often assume a global unique camera and seldom consider scenarios with multiple cameras. Besides, some pose-robust methods still remain susceptible to suboptimal solutions when poses are poor initialized. In this paper, we propose MC-NeRF, a method can jointly optimize both intrinsic and extrinsic parameters for bundle-adjusting Neural Radiance Fields. Firstly, we conduct a theoretical analysis to tackle the degenerate case and coupling issue that arise from the joint optimization between intrinsic and extrinsic parameters. Secondly, based on the proposed solutions, we introduce an efficient calibration image acquisition scheme for multi-camera systems, including the design of calibration object. Lastly, we present a global end-to-end network with training sequence that enables the regression of intrinsic and extrinsic parameters, along with the rendering network. Moreover, most existing datasets are designed for unique camera, we create a new dataset that includes four different styles of multi-camera acquisition systems, allowing readers to generate custom datasets. Experiments confirm the effectiveness of our method when each image corresponds to different camera parameters. Specifically, we adopt up to 110 images with 110 different intrinsic and extrinsic parameters, to achieve 3D scene representation without providing initial poses. The Code and supplementary materials are available at https://in2-viaun.github.io/MC-NeRF.
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