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docs: generated API documentation (483 pages)
2026-07-24 11:55:47 +00:00

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<a href="#namespaces">命名空间</a> &#124;
<a href="#func-members">函数</a> </div>
<div class="headertitle">
<div class="title">sdf_torch.h 文件参考<div class="ingroups"><a class="el" href="group__sdf.html">SDF 模块 (vde::sdf)</a></div></div> </div>
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<p>SDF 批量求值与梯度计算（CPU 后端）
<a href="#details">更多...</a></p>
<div class="textblock"><code>#include &quot;<a class="el" href="sdf__gradient_8h_source.html">vde/sdf/sdf_gradient.h</a>&quot;</code><br />
<code>#include &quot;<a class="el" href="sdf__tree_8h_source.html">vde/sdf/sdf_tree.h</a>&quot;</code><br />
<code>#include &quot;<a class="el" href="point_8h_source.html">vde/core/point.h</a>&quot;</code><br />
<code>#include &lt;vector&gt;</code><br />
</div><div class="textblock"><div class="dynheader">
sdf_torch.h 的引用(Include)关系图:</div>
<div class="dyncontent">
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<p><a href="sdf__torch_8h_source.html">浏览源代码.</a></p>
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命名空间</h2></td></tr>
<tr class="memitem:namespacevde"><td class="memItemLeft" align="right" valign="top"> &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacevde.html">vde</a></td></tr>
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<tr class="memitem:a8467f2e799c2975d5d84f79c9ca43396"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacevde_1_1sdf.html#a8467f2e799c2975d5d84f79c9ca43396">vde::sdf::evaluate_batch</a> (const SdfNodePtr &amp;root, const double *points, int n, double *distances)</td></tr>
<tr class="memdesc:a8467f2e799c2975d5d84f79c9ca43396"><td class="mdescLeft">&#160;</td><td class="mdescRight">在批处理模式下求值 SDF <a href="namespacevde_1_1sdf.html#a8467f2e799c2975d5d84f79c9ca43396">更多...</a><br /></td></tr>
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<tr class="memitem:a08c23db981f57462da9852b6261a91c6"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacevde_1_1sdf.html#a08c23db981f57462da9852b6261a91c6">vde::sdf::gradient_batch</a> (const SdfNodePtr &amp;root, const double *points, int n, double *gradients)</td></tr>
<tr class="memdesc:a08c23db981f57462da9852b6261a91c6"><td class="mdescLeft">&#160;</td><td class="mdescRight">在批处理模式下求值 SDF 的空间梯度 <a href="namespacevde_1_1sdf.html#a08c23db981f57462da9852b6261a91c6">更多...</a><br /></td></tr>
<tr class="separator:a08c23db981f57462da9852b6261a91c6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad4bef2ad0812ae53c05834c6eb595deb"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="namespacevde_1_1sdf.html#ad4bef2ad0812ae53c05834c6eb595deb">vde::sdf::evaluate_with_gradient_batch</a> (const SdfNodePtr &amp;root, const double *points, int n, double *distances, double *gradients)</td></tr>
<tr class="memdesc:ad4bef2ad0812ae53c05834c6eb595deb"><td class="mdescLeft">&#160;</td><td class="mdescRight">一次遍历同时求值 SDF 值和梯度 <a href="namespacevde_1_1sdf.html#ad4bef2ad0812ae53c05834c6eb595deb">更多...</a><br /></td></tr>
<tr class="separator:ad4bef2ad0812ae53c05834c6eb595deb"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><p>SDF 批量求值与梯度计算（CPU 后端） </p>
<p>提供吞吐量优化的 SDF 批量求值接口，适合大量点或实时场景。</p>
<h2><a class="anchor" id="autotoc_md32"></a>
设计意图</h2>
<p>虽然命名为 sdf_torch，但本头的实现是纯 C++ CPU 版本， 不依赖 PyTorch。接口设计为"可替换为 CUDA/Torch 后端"的占位， 函数签名与假设的 GPU 版本保持一致。</p>
<h2><a class="anchor" id="autotoc_md33"></a>
数据布局</h2>
<ul>
<li><b>输入数组:</b> x,y,z 交替存储（SoA 未使用），n 个点 × 3 个 double</li>
<li><b>输出距离:</b> 连续 n 个 double</li>
<li><b>输出梯度:</b> 连续 3n 个 double，x,y,z 交替存储</li>
</ul>
<h2><a class="anchor" id="autotoc_md34"></a>
使用场景</h2>
<ul>
<li>Ray-marching：沿射线采样大量点</li>
<li>网格求值：Marching Cubes 前的密集采样</li>
<li>可视化：距离场的 heatmap 生成</li>
<li>物理仿真：碰撞检测批量查询 </li>
</ul>
<p class="definition">在文件 <a class="el" href="sdf__torch_8h_source.html">sdf_torch.h</a> 中定义.</p>
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