Gaussian Splatting has emerged as a prominent model for constructing 3D
representations from images across diverse domains. However, the efficiency of
the 3D Gaussian Splatting rendering pipeline relies on several simplifications.
Notably, reducing Gaussian to 2D splats with a single view-space depth
introduces popping and blending artifacts during view rotation. Addressing this
issue requires accurate per-pixel depth computation, yet a full per-pixel sort
proves excessively costly compared to a global sort operation. In this paper,
we present a novel hierarchical rasterization approach that systematically
resorts and culls splats with minimal processing overhead. Our software
rasterizer effectively eliminates popping artifacts and view inconsistencies,
as demonstrated through both quantitative and qualitative measurements.
Simultaneously, our method mitigates the potential for cheating view-dependent
effects with popping, ensuring a more authentic representation. Despite the
elimination of cheating, our approach achieves comparable quantitative results
for test images, while increasing the consistency for novel view synthesis in
motion. Due to its design, our hierarchical approach is only 4% slower on
average than the original Gaussian Splatting. Notably, enforcing consistency
enables a reduction in the number of Gaussians by approximately half with
nearly identical quality and view-consistency. Consequently, rendering
performance is nearly doubled, making our approach 1.6x faster than the
original Gaussian Splatting, with a 50% reduction in memory requirements.

通过分层光栅化方法提高高斯喷洒的渲染效率，消除了弹出伪影和视图不一致问题，并在测试图像中获得相当的定量结果，在新视角综合运动中提供一致性。通过同时减少高斯函数数量来提高性能，并减少了一半的内存需求，使得我们的方法比原始的高斯喷洒方法快 1.6 倍。

StopThePop: 基于排序的高斯飞溅 用于保持视图一致性的实时渲染

StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time  Rendering

We present a fully data-driven method to compute depth from diverse monocular
video sequences that contain large amounts of non-rigid objects, e.g., people.
In order to learn reconstruction cues for non-rigid scenes, we introduce a new
dataset consisting of stereo videos scraped in-the-wild. This dataset has a
wide variety of scene types, and features large amounts of nonrigid objects,
especially people. From this, we compute disparity maps to be used as
supervision to train our approach. We propose a loss function that allows us to
generate a depth prediction even with unknown camera intrinsics and stereo
baselines in the dataset. We validate the use of large amounts of Internet
video by evaluating our method on existing video datasets with depth
supervision, including SINTEL, and KITTI, and show that our approach
generalizes better to natural scenes.

利用全数据驱动的方法计算深度，通过对非刚性场景开发一种新数据集，其中包括大量的非刚性对象，特别是人，提出了一种损失函数进行深度预测，评估方法并表明该方法对自然场景具有更好的泛化能力。