pytorch3d学习之pytorch3d.ops
pytorch3d.ops是pytorch提供的一些关于3d数据，即计算机图形学的一些运算的包。
1.pytorch3d.ops.ball_query（）
pytorch3d.ops.ball_query(p1: torch.Tensor, p2: torch.Tensor, lengths1: Optional[torch.Tensor] = None, lengths2: Optional[torch.Tensor] = None, K: int = 500, radius: float = 0.2, return_nn: bool = True)Ball Query is an alternative to KNN. It can be used to find all points in p2 that are within a specified radius to the query point in p1 (with an upper limit of K neighbors).
The neighbors returned are not necssarily the nearest to the point in p1, just the first K values in p2 which are within the specified radius.
在p2中找距离p1的在特定半径范围内的点，它不一定是k最邻近点。
2.pytorch3d.ops.cubify()
pytorch3d.ops.cubify(voxels, thresh, device=None, align: str = 'topleft') → pytorch3d.structures.meshes.Meshes[source]
Converts a voxel to a mesh by replacing each occupied voxel with a cube consisting of 12 faces and 8 vertices. Shared vertices are merged, and internal faces are removed. :param voxels: A FloatTensor of shape (N, D, H, W) containing occupancy probabilities. :param thresh: A scalar threshold. If a voxel occupancy is larger than
将voxel转为mesh
3.pytorch3d.ops.knn_gather
pytorch3d.ops.knn_gather(x: torch.Tensor, idx: torch.Tensor, lengths: Optional[torch.Tensor] = None)[source]
A helper function for knn that allows indexing a tensor x with the indices idx returned by knn_points.
For example, if dists, idx = knn_points(p, x, lengths_p, lengths, K) where p is a tensor of shape (N, L, D) and x a tensor of shape (N, M, D), then one can compute the K nearest neighbors of p with p_nn = knn_gather(x, idx, lengths). It can also be applied for any tensor x of shape (N, M, U) where U != D.
返回p的k最邻近点以及点的坐标·
4.pytorch3d.ops.knn_points
pytorch3d.ops.knn_points(p1: torch.Tensor, p2: torch.Tensor, lengths1: Optional[torch.Tensor] = None, lengths2: Optional[torch.Tensor] = None, K: int = 1, version: int = -1, return_nn: bool = False, return_sorted: bool = True) → pytorch3d.ops.knn.KNN[source]
Returns: dists –
Tensor of shape (N, P1, K) giving the squared distances to
the nearest neighbors. This is padded with zeros both where a cloud in p2 has fewer than K points and where a cloud in p1 has fewer than P1 points.
idx: LongTensor of shape (N, P1, K) giving the indices of the
K nearest neighbors from points in p1 to points in p2. Concretely, if p1_idx[n, i, k] = j then p2[n, j] is the k-th nearest neighbors to p1[n, i] in p2[n]. This is padded with zeros both where a cloud in p2 has fewer than K points and where a cloud in p1 has fewer than P1 points.
nn: Tensor of shape (N, P1, K, D) giving the K nearest neighbors in p2 for
each point in p1. Concretely, p2_nn[n, i, k] gives the k-th nearest neighbor for p1[n, i]. Returned if return_nn is True. The nearest neighbors are collected using knn_gather
which is a helper function that allows indexing any tensor of shape (N, P2, U) with the indices p1_idx returned by knn_points. The output is a tensor of shape (N, P1, K, U).
5.pytorch3d.ops.corresponding_points_alignment
pytorch3d.ops.corresponding_points_alignment(X: Union[torch.Tensor, Pointclouds], Y: Union[torch.Tensor, Pointclouds], weights: Union[torch.Tensor, List[torch.Tensor], None] = None, estimate_scale: bool = False, allow_reflection: bool = False, eps: float = 1e-09) → pytorch3d.ops.points_alignment.SimilarityTransform[source]
Finds a similarity transformation (rotation R, translation T and optionally scale s) between two given sets of corresponding d-dimensional points X and Y such that:
s[i] X[i] R[i] + T[i] = Y[i],
for all batch indexes i in the least squares sense.
The algorithm is also known as Umeyama [1].
计算两个向量x，y他们之间的相似变换矩阵，包括旋转矩阵R， 平移矩阵T，缩放矩阵s。
6.拉普拉斯矩阵计算
pytorch3d.ops.cot_laplacian(verts: torch.Tensor, faces: torch.Tensor, eps: float = 1e-12) → Tuple[torch.Tensor, torch.Tensor][source]Returns the Laplacian matrix with cotangent weights and the inverse of the face areas.
Parameters:
- verts – tensor of shape (V, 3) containing the vertices of the graph
- faces – tensor of shape (F, 3) containing the vertex indices of each face
Returns: 2-element tuple containing - L: Sparse FloatTensor of shape (V,V) for the Laplacian matrix.
Here, L[i, j] = cot a_ij + cot b_ij iff (i, j) is an edge in meshes. See the description above for more clarity.
inv_areas: FloatTensor of shape (V,) containing the inverse of sum of
face areas containing each vertex
pytorch3d.ops.laplacian(verts: torch.Tensor, edges: torch.Tensor) → torch.Tensor[source]Computes the laplacian matrix. The definition of the laplacian is L[i, j] = -1 , if i == j L[i, j] = 1 / deg(i) , if (i, j) is an edge L[i, j] = 0 , otherwise where deg(i) is the degree of the i-th vertex in the graph.
Parameters:
- verts – tensor of shape (V, 3) containing the vertices of the graph
- edges – tensor of shape (E, 2) containing the vertex indices of each edge
Returns: L – Sparse FloatTensor of shape (V, V)
pytorch3d.ops.norm_laplacian(verts: torch.Tensor, edges: torch.Tensor, eps: float = 1e-12) → torch.Tensor[source]Norm laplacian computes a variant of the laplacian matrix which weights each affinity with the normalized distance of the neighboring nodes. More concretely, L[i, j] = 1. / wij where wij = ||vi - vj|| if (vi, vj) are neighboring nodes
Parameters:
- verts – tensor of shape (V, 3) containing the vertices of the graph
- edges – tensor of shape (E, 2) containing the vertex indices of each edge
Returns: L – Sparse FloatTensor of shape (V, V)
7. ICP算法
pytorch3d.ops.iterative_closest_point(X: Union[torch.Tensor, Pointclouds], Y: Union[torch.Tensor, Pointclouds], init_transform: Optional[pytorch3d.ops.points_alignment.SimilarityTransform] = None, max_iterations: int = 100, relative_rmse_thr: float = 1e-06, estimate_scale: bool = False, allow_reflection: bool = False, verbose: bool = False) → pytorch3d.ops.points_alignment.ICPSolution[source]Executes the iterative closest point (ICP) algorithm [1, 2] in order to find a similarity transformation (rotation R, translation T, and optionally scale s) between two given differently-sized sets of d-dimensional points X and Y, such that:
pytorch3d学习之pytorch3d.ops相关推荐
- Pytorch3d学习记录
Cameras照相机 Camera Coordinate Systems 相机坐标系统 世界坐标 (Y指向上方,X指向左方,Z指向里面) 相机视图坐标(相机坐标) Camera view coordi ...
- Pytorch学习之torch----Reduction Ops
1. torch.cumprod(input, dim, out=None) 说明: 返回输入沿指定维度的累积积.如果输入是一个N元向量,则结果也是一个N元向量,第i个输出元素值为 yi=x1∗x2∗ ...
- Facebook 开源 3D 深度学习函数库 PyTorch3D，也可用于二维场景
渲染是计算机图形学中的核心内容,它可将 3D 模型转换为 2D 图像.这也是在 3D 场景属性(scene properties)和 2D 图像像素之间建立桥接的常规手段.不过传统渲染引擎无法进行微分 ...
- FaceBook开源PyTorch3D：基于PyTorch的新3D计算机视觉库
关注上方"深度学习技术前沿",选择"星标公众号", 资源干货,第一时间送达! PyTorch 进军三维计算机视觉了,现在,你的神经网络可以更轻松地和 3D 数据 ...
- pytorch3d在linux下安装
首先安装虚拟环境和依赖 conda create -n pytorch3d python=3.8 conda activate pytorch3d conda install -c pytorch p ...
- anaconda环境更改gcc版本并编译Pytorch3D 0.4.0
项目需要编译pytorch3D 0.4.0且需要gcc 7以上且8以下的版本(尝试了5.4.0和12.2.0均报错),所以打算安装7.3.0版本的gcc,但是服务器没有root权限,所以只能在cond ...
- 在Ubuntu20.04系统anaconda3的环境下安装pytorch3d+opencv
原来的pytorch3d和opencv的编程环境莫名崩掉了,装了很久都没重新装成功,这次记录下来,以后再有问题就翻翻笔记. Preliminaries 由于后续要用到cuda,所以首先查看当前的cud ...
- 安装pytorch3d 遇到的问题，以及解决方案
安装pytorch3d 遇到的问题,以及解决方案 遇到的问题 ImportError: /usr/lib/libcudart.so.10.1: version `libcudart.so.10.1' ...
- ubuntu20.04配置FrankMocap实现3D人体姿态估计
一.初始环境配置 1.ubuntu20.04配置显卡驱动 以我的这篇文章为例子,显卡RTX2060及以下的都可以使用我的方法快速完成配置,RTX2060以上的我尚未进行尝试,请自行斟酌尝试. 联想拯救 ...
最新文章
- 技术图文：如何改进算法的运行效率？
- FlashDevelop+aswing开发AS2程序
- IT职场最受欢迎的十大晋升秘决
- 理论基础 —— 索引 —— 稠密索引
- MySQL中查询字段为空或者为null方法
- SQL Serevr 2012 安装教程
- 如何修复win7蓝牙服务器,快速解决win7系统蓝牙驱动的修复方法
- SharePoint 2019 图文安装教程
- win10系统无打印机服务器,Print Spooler服务未在Windows 10中运行
- 整理了一份嵌入式软件工具清单！
- android mt4 macd,超准确的4小时MACD交易策略
- 波形图控件html,[转载]LabVIEW中的波形图(Waveform Chart)详解
- 软件测试面试宝典【测试流程、数据库、Linux、测试工具、自动化、性能测试】
- JETT（五）-支持Excel公式
- css画心形原理,CSS画心形的三种方法
- 最短路径--狄克斯特拉（Dijkstra）算法
- 工作积累⑨——从丁香医生增长看地推的重要性
- [ 华为云 ] 云计算中Region、VPC、AZ 是什么，他们又是什么关系，应该如何抉择
- java服务器开发心得
- java提出word和pdf等文件的信息