Currently, 3D rendering for large-scale free camera trajectories, namely, arbitrary input camera trajectories, poses significant challenges: 1) The distribution and observation angles of the cameras are irregular, and various types of scenes are included in the free trajectories; 2) Processing the entire point cloud and all images at once for large-scale scenes requires a substantial amount of GPU memory. This paper presents a Toy-GS method for accurately rendering large-scale free camera trajectories. Specifically, we propose an adaptive spatial division approach for free trajectories to divide cameras and the sparse point cloud of the entire scene into various regions according to camera poses. Training each local Gaussian in parallel for each area enables us to concentrate on texture details and minimize GPU memory usage. Next, we use the multi-view constraint and position-aware point adaptive control (PPAC) to improve the rendering quality of texture details. In addition, our regional fusion approach combines local and global Gaussians to enhance rendering quality with an increasing number of divided areas. Extensive experiments have been carried out to confirm the effectiveness and efficiency of Toy-GS, leading to state-of-the-art results on two public large-scale datasets as well as our SCUTic dataset. Our proposal demonstrates an enhancement of 1.19 dB in PSNR and conserves 7 G of GPU memory when compared to various benchmarks.
目前，对于大规模自由相机轨迹的3D渲染（即任意输入相机轨迹）存在显著挑战：1）相机的分布和观测角度不规则，自由轨迹中包含多种类型的场景；2）处理大规模场景的整个点云和所有图像需要大量的GPU内存。本文提出了一种名为 Toy-GS 的方法，用于准确渲染大规模自由相机轨迹。具体而言，我们提出了一种针对自由轨迹的自适应空间划分方法，根据相机位姿将整个场景的相机和稀疏点云划分为不同区域。通过对每个区域的局部高斯进行并行训练，我们能够专注于纹理细节，并最小化GPU内存使用。 接下来，我们利用多视图约束和位置感知点自适应控制（PPAC）来提高纹理细节的渲染质量。此外，我们的区域融合方法结合了局部和全局高斯，随着划分区域数量的增加进一步增强渲染质量。广泛的实验验证了 Toy-GS 的有效性和效率，在两个公共的大规模数据集以及我们的 SCUTic 数据集上实现了最先进的性能。与各种基准方法相比，我们的方法在 PSNR 上提升了1.19 dB，同时节省了7 GB的GPU内存。