In the wake of many new ML-inspired approaches for reconstructing and
representing high-quality 3D content, recent hybrid and explicitly learned
representations exhibit promising performance and quality characteristics.
However, their scaling to higher dimensions is challenging, e.g. when
accounting for dynamic content with respect to additional parameters such as
material properties, illumination, or time. In this paper, we tackle these
challenges for an explicit representations based on Gaussian mixture models.
With our solutions, we arrive at efficient fitting of compact N-dimensional
Gaussian mixtures and enable efficient evaluation at render time: For fast
fitting and evaluation, we introduce a high-dimensional culling scheme that
efficiently bounds N-D Gaussians, inspired by Locality Sensitive Hashing. For
adaptive refinement yet compact representation, we introduce a loss-adaptive
density control scheme that incrementally guides the use of additional capacity
towards missing details. With these tools we can for the first time represent
complex appearance that depends on many input dimensions beyond position or
viewing angle within a compact, explicit representation optimized in minutes
and rendered in milliseconds.

我们使用高斯混合模型构建了一种高效的显式表示方法，可以在短时间内优化和渲染复杂的外观，该方法能够应对高维度挑战和缺失细节，并适用于快速拟合和评估。

高维函数拟合的 N 维高斯模型

N-Dimensional Gaussians for Fitting of High Dimensional Functions

The explicit neural radiance field (NeRF) has gained considerable interest
for its efficient training and fast inference capabilities, making it a
promising direction such as virtual reality and gaming. In particular,
PlenOctree (POT)[1], an explicit hierarchical multi-scale octree
representation, has emerged as a structural and influential framework. However,
POT's fixed structure for direct optimization is sub-optimal as the scene
complexity evolves continuously with updates to cached color and density,
necessitating refining the sampling distribution to capture signal complexity
accordingly. To address this issue, we propose the dynamic PlenOctree DOT,
which adaptively refines the sample distribution to adjust to changing scene
complexity. Specifically, DOT proposes a concise yet novel hierarchical feature
fusion strategy during the iterative rendering process. Firstly, it identifies
the regions of interest through training signals to ensure adaptive and
efficient refinement. Next, rather than directly filtering out valueless nodes,
DOT introduces the sampling and pruning operations for octrees to aggregate
features, enabling rapid parameter learning. Compared with POT, our DOT
outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$
parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$
Temples, respectively. Project homepage:this https URL
[1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance
fields." Proceedings of the IEEE/CVF International Conference on Computer
Vision. 2021.

提出了一种动态的 PlenOctree DOT 方法，通过自适应细化采样分布来适应不断变化的场景复杂度，并通过层次化特征融合策略提高了视觉质量，减少了参数数量并提供了更高的帧率。

动态 PlenOctree 在显式 NeRF 中的自适应采样细化

Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

Most image captioning frameworks generate captions directly from images,
learning a mapping from visual features to natural language. However, editing
existing captions can be easier than generating new ones from scratch.
Intuitively, when editing captions, a model is not required to learn
information that is already present in the caption (i.e. sentence structure),
enabling it to focus on fixing details (e.g. replacing repetitive words). This
paper proposes a novel approach to image captioning based on iterative adaptive
refinement of an existing caption. Specifically, our caption-editing model
consisting of two sub-modules: (1) EditNet, a language module with an adaptive
copy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism
(SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder. These components
enable our model to directly copy from and modify existing captions.
Experiments demonstrate that our new approach achieves state-of-art performance
on the MS COCO dataset both with and without sequence-level training.

本文提出了一种基于迭代自适应改进现有标题的图像字幕生成的新方法，该方法包括两个子模块：一个具有自适应复制机制和选择性复制存储器注意机制的语言模块 EditNet 和一个基于 LSTM 的去噪自编码器 DCNet，实验表明我们的方法在 MS COCO 数据集上取得了最先进的性能。