HViktorTsoi

import copy
import time

import open3d as o3d
import os
from collections import defaultdict
import numpy as np
import numpy.linalg as LA
import gtsam
import tqdm

HViktorTsoi

import numpy.linalg as LA
import numpy as np

def any_LiDAR_to_ring(pc, num_beams=32, ring_height=8e-4):
    """
    convert any type of LiDAR point cloud to ring-based LiDAR style
    :param pc: input point cloud, shape of Nx4(x,y,z,intensity)
    :param num_beams: number of beams
    :param ring_height: the "line width" of a ring
    :return: ring-stype point cloud, shape of Nx5(x,y,z,intensity, ring ID)

ialhashim

# Original Matlab code https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
#
#
# Python port of depth filling code from NYU toolbox
# Speed needs to be improved
#
# Uses 'pypardiso' solver 
#
import scipy
import skimage

royshil

import cv2
import numpy as np

def cylindricalWarp(img, K):
    """This function returns the cylindrical warp for a given image and intrinsics matrix K"""
    h_,w_ = img.shape[:2]
    # pixel coordinates
    y_i, x_i = np.indices((h_,w_))
    X = np.stack([x_i,y_i,np.ones_like(x_i)],axis=-1).reshape(h_*w_,3) # to homog
    Kinv = np.linalg.inv(K) 

tonejito

|                                       |
|:-------------------------------------:|
| ![](https://goo.gl/1R3T6h "Tonejito") |

ryerh

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

Tmux 快捷键 & 速查表 & 简明教程

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：

CarloNicolini

"""
 RALIGN - Rigid alignment of two sets of points in k-dimensional
          Euclidean space.  Given two sets of points in
          correspondence, this function computes the scaling,
          rotation, and translation that define the transform TR
          that minimizes the sum of squared errors between TR(X)
          and its corresponding points in Y.  This routine takes
          O(n k^3)-time.

 Inputs:

ashwin

### CMakeLists.txt for CUDA

cmake_minimum_required(VERSION 2.8)
find_package(CUDA QUIET REQUIRED)

# Pass options to NVCC
set(
    CUDA_NVCC_FLAGS
    ${CUDA_NVCC_FLAGS};
    -O3 -gencode arch=compute_22,code=sm_22