Modeling dynamic, large-scale urban scenes is challenging due to their highly
intricate geometric structures and unconstrained dynamics in both space and
time. Prior methods often employ high-level architectural priors, separating
static and dynamic elements, resulting in suboptimal capture of their
synergistic interactions. To address this challenge, we present a unified
representation model, called Periodic Vibration Gaussian (PVG). PVG builds upon
the efficient 3D Gaussian splatting technique, originally designed for static
scene representation, by introducing periodic vibration-based temporal
dynamics. This innovation enables PVG to elegantly and uniformly represent the
characteristics of various objects and elements in dynamic urban scenes. To
enhance temporally coherent representation learning with sparse training data,
we introduce a novel flow-based temporal smoothing mechanism and a
position-aware adaptive control strategy. Extensive experiments on Waymo Open
Dataset and KITTI benchmarks demonstrate that PVG surpasses state-of-the-art
alternatives in both reconstruction and novel view synthesis for both dynamic
and static scenes. Notably, PVG achieves this without relying on manually
labeled object bounding boxes or expensive optical flow estimation. Moreover,
PVG exhibits 50/6000-fold acceleration in training/rendering over the best
alternative.

建模动态的大规模城市场景是具有挑战性的，本文提出了一种统一的表示模型，Periodic Vibration Gaussian (PVG)，通过引入周期振动基于时间的动力学，以高效及统一的方式表示动态城市场景中各种物体和元素的特征，通过稀疏训练数据引入了一种新颖的基于流的时间平滑机制和位置感知的自适应控制策略，实验证明 PVG 在重建和新视角合成方面优于现有最先进的方法，且无需依赖手动标注的物体边界框或昂贵的光流估计，此外，PVG 在训练 / 渲染速度上比最佳替代方法加速了 50/6000 倍。

周期振动高斯：动态城市场景重建与实时渲染

Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and  Real-time Rendering

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes.
Prior work tends to reconstruct single video clips of short durations (up to 10
seconds). Two reasons are that such methods (a) tend to scale linearly with the
number of moving objects and input videos because a separate model is built for
each and (b) tend to require supervision via 3D bounding boxes and panoptic
labels, obtained manually or via category-specific models. As a step towards
truly open-world reconstructions of dynamic cities, we introduce two key
innovations: (a) we factorize the scene into three separate hash table data
structures to efficiently encode static, dynamic, and far-field radiance
fields, and (b) we make use of unlabeled target signals consisting of RGB
images, sparse LiDAR, off-the-shelf self-supervised 2D descriptors, and most
importantly, 2D optical flow.
Operationalizing such inputs via photometric, geometric, and feature-metric
reconstruction losses enables SUDS to decompose dynamic scenes into the static
background, individual objects, and their motions. When combined with our
multi-branch table representation, such reconstructions can be scaled to tens
of thousands of objects across 1.2 million frames from 1700 videos spanning
geospatial footprints of hundreds of kilometers, (to our knowledge) the largest
dynamic NeRF built to date.
We present qualitative initial results on a variety of tasks enabled by our
representations, including novel-view synthesis of dynamic urban scenes,
unsupervised 3D instance segmentation, and unsupervised 3D cuboid detection. To
compare to prior work, we also evaluate on KITTI and Virtual KITTI 2,
surpassing state-of-the-art methods that rely on ground truth 3D bounding box
annotations while being 10x quicker to train.

该研究将神经辐射场（NeRF）扩展到动态大规模城市场景。使用有标记和无标记的数据，通过光度、几何和特征度量重建来将动态场景分解为静态背景、个体对象和它们的运动，并在 novel-view synthesis, unsupervised 3D instance segmentation 和 unsupervised 3D cuboid detection 等多种任务中显示出优异性能。