Gaussian Splatting in Style(Related Work)

3D Scene representation

Complex 3D scenes can be represented in many ways. One of the most common and well known ways is that of using point clouds. Similar to point clouds, some works use a voxel grid for representing a 3D scene.

Voxel grids are not the most ideal choice for scene representation due to their high memory footprint.

Neural implicit scene based methods, led by some works making use of signed distance fields(SDFs) and occupancies respectively, helped solve this memory issue.

In principle, implicit neural representations can generate meshes and surfaces upto an arbitrarily high resolution.

Recently, NeuralAngelo set a new benchmark as it could recover a highly detailed 3D surface of a large-scale scene from a set of input images.

Following a similar direction neural radiance fields, also known as NeRFs[50] are also an implicit method.


Novel View Synthesis

The task of generating unseen views of an object/scene given a collection of input images is known as novel view synthesis.

Classical works aimed at generating novel views directly from the given set of multi-view ima

### 高斯点绘技术概述 高斯点绘(Gaussian Splatting)是一种用于计算机图形学和可视化中的高效渲染方法,通过将几何体表示为一组加权高斯分布来增强效率、交互性和真实感[^1]。 #### 技术原理 在具体实施过程中,在给定的场景数据上,系统会预测对应于该输入的一系列高斯点。这些高斯点不仅包含了位置信息还携带了颜色和其他属性参数。此过程通常基于特定算法框架完成,比如pixelSplat实现方案中提到的方法[^2]。 对于大规模场景的应用而言,所采用的训练策略允许将由不同视角下的图像对所产生的全部高斯点串联起来形成完整的表达形式,从而支持更广泛的环境建模需求。 ```python import numpy as np def generate_gaussian_points(image_pairs): gaussian_points = [] for pair in image_pairs: # 基于pair生成单个高斯点 point = compute_gaussian_point(pair) # 添加到总集合中 gaussian_points.append(point) return gaussian_points def compute_gaussian_point(pair): mean_position = calculate_mean_from_pair(pair) # 计算均值向量 covariance_matrix = estimate_covariance(mean_position, pair) # 估计协方差矩阵 return {'mean': mean_position, 'covariance': covariance_matrix} ``` #### 应用实例 为了更好地捕捉物体表面附近的细节特征以及体积效果(如毛发或草),一种改进方式是从初始优化阶段提取基础网格结构并在此基础上构建具有可变厚度适应层的高斯分布模型[^5]。这种方法可以显著提高复杂纹理的表现力。
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