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| Given noisy point clouds and inaccurate camera poses, our constrained optimization approach reconstructs the 3D scene in Gaussian Splatting with high visual quality. |
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| Given noisy point clouds and inaccurate camera poses, our constrained optimization approach reconstructs the 3D scene in Gaussian Splatting with high visual quality. |
| 3D Gaussian Splatting (3DGS) is a powerful reconstruction technique, but it needs to be initialized from accurate camera poses and high-fidelity point clouds. Typically, the initialization is taken from Structure-from-Motion (SfM) algorithms; however, SfM is time-consuming and restricts the application of 3DGS in real-world scenarios and large-scale scene reconstruction. We introduce a constrained optimization method for simultaneous camera pose estimation and 3D reconstruction that does not require SfM support. Core to our approach is decomposing a camera pose into a sequence of camera-to-(device-)center and (device-)center-to-world optimizations. To facilitate, we propose two optimization constraints conditioned to the sensitivity of each parameter group and restricts each parameter's search space. In addition, as we learn the scene geometry directly from the noisy point clouds, we propose geometric constraints to improve the reconstruction quality. Experiments demonstrate that the proposed method significantly outperforms the existing (multi-modal) 3DGS baseline and methods supplemented by COLMAP on both our collected dataset and two public benchmarks. |
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| Qualitative example of camera poses and colored point clouds obtained from our multi-camera SLAM system. |
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| Illustration of camera intrinsic optimization. (a) In monocular setting, inaccurate intrinsic parameters could be corrected by adjusting the camera pose, eg. shifting the camera origin right by T. (b) This approach is not feasible for multi-cameras under extrinsic constraints like autonomous cars or SLAM devices. |
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| Illustration of our camera decomposition scheme. (a) Initial noisy point cloud from SLAM setup. (b) and (d) Optimization procedures of device-to-world and camera-to-device transformations. (c) Refined point cloud from our constrained optimization approach, showing improved visual quality. |
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| Illustration of the log-barrier method. Lower and upper bounds are predefined based on initial SLAM estimation. At the start of the optimization, the barrier imposes a strong penalty for significant deviations from the initial estimate. As temperature increases, it transforms into a well-function, allowing the parameter to fully explore the feasible region. |
