Snowflake now supports geospatial datatype.It supports geographical features like point,line,Polygon.
But wanted to know ,whether it supports for spherical feature?
How to plot a spherical feature in Snowflake geospatial datatype?
According to the Snowflake documentation it only supports WGS84 (EPSG:4326), which does not have a z (height/elevation) coordinate, making it impossible to represent a sphere, which is a 3d shape. Approximating the 2d projection, a circle, should be somewhat possible.
A lot of geospatial engines have a function called buffer (or some variation thereof), which when performed on a point would give you an approximation of a circle. Depending on what you are doing, this might be what you need. Unfortunately, it doesn't look like Snowflake has a buffer function as of July 2020.
If all you need to do is detect whether a point p lies within a disc of radius r around a point c, you can use the ST_DWITHIN function to accomplish that goal. You can find the documentation for that function here. Note that you must supply your radius in meters.
Related
Say I had a point cloud with n number of points in 3d space(relatively densely packed together). What is the most efficient way to create a surface that goes contains every single point in it and lets me calculate values such as the normal and curvature at some point on the surface that was created? I also need to be able to create this surface as fast as possible(a few milliseconds hopefully working with python) and it can be assumed that n < 1000.
There is no "most efficient and effective" way (this is true of any problem in any domain).
In the first place, the surface you have in mind is not mathematically defined uniquely.
A possible approach is by means of the so-called Alpha-shapes, implemented either from a Delaunay tetrahedrization, or by the ball-pivoting method. For other methods, lookup "mesh reconstruction" or "surface reconstruction".
On another hand, normals and curvature can be computed locally, from neighbors configurations, without reconstructing a surface (though there is an ambiguity on the orientation of the normals).
I could suggest Nina Amenta's Power Crust algorithm (link to code), or also meshlab suite, which can compute the curvatures too.
What is the difference between the arango function - DISTANCE() and GE0_DISTANCE(). I know both of them calculates distance using haversines formula.
Thanks,
Nilotpal
Both are used for two different purposes
DISTANCE(latitude1, longitude1, latitude2, longitude2) → distance
The value is computed using the haversine formula, which is based on a spherical Earth model. It’s fast to compute and is accurate to around 0.3%, which is sufficient for most use cases such as location-aware services.
GEO_DISTANCE(geoJsonA, geoJsonB, ellipsoid) → distance
Return the distance between two GeoJSON objects, measured from the centroid of each shape. For a list of supported types see the geo index page. (Ref: https://www.arangodb.com/docs/3.8/aql/functions-geo.html#geo-index-functions)
This GeoJSON objects can be anything like GEO_LINESTRING, GEO_MULTILINESTRING, GEO_MULTIPOINT, GEO_POINT, GEO_POLYGON and GEO_MULTIPOLYGON - Reference<2>
Reference:
https://www.arangodb.com/docs/3.8/aql/functions-geo.html#geo-utility-functions
https://www.arangodb.com/docs/3.8/aql/functions-geo.html#geojson-constructors
Is there a library in python or c++ that is capable of estimating normals of point clouds in a consistent way?
In a consistent way I mean that the orientation of the normals is globally preserved over the surface.
For example, when I use python open3d package:
downpcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(
radius=4, max_nn=300))
I get an inconsistent results, where some of the normals point inside while the rest point outside.
many thanks
UPDATE: GOOD NEWS!
The tangent plane algorithm is now implemented in Open3D!
The source code and the documentation.
You can just call pcd.orient_normals_consistent_tangent_plane(k=15).
And k is the knn graph parameter.
Original answer:
Like Mark said, if your point cloud comes from multiple depth images, then you can call open3d.geometry.orient_normals_towards_camera_location(pcd, camera_loc) before concatenating them together (assuming you're using python version of Open3D).
However, if you don't have that information, you can use the tangent plane algorithm:
Build knn-graph for your point cloud.
The graph nodes are the points. Two points are connected if one is the other's k-nearest-neighbor.
Assign weights to the edges in the graph.
The weight associated with edge (i, j) is computed as 1 - |ni ⋅ nj|
Generate the minimal spanning tree of the resulting graph.
Rooting the tree at an initial node,
traverse the tree in depth-first order, assigning each node an
orientation that is consistent with that of its parent.
Actually the above algorithm comes from Section 3.3 of Hoppe's 1992
SIGGRAPH paper Surface Reconstruction from Unorganized Points. The algorithm is also open sourced.
AFAIK the algorithm does not guarantee a perfect orientation, but it should be good enough.
If you know the viewpoint from where each point was captured, it can be used to orient the normals.
I assume that this not the case - so given your situation, which seems rather watertight and uniformly sampled, mesh reconstruction is promising.
PCL library offers many alternatives in the surface module. For the sake of normal estimation, I would start with either:
ConcaveHull
Greedy projection triangulation
Although simple, they should be enough to produce a single coherent mesh.
Once you have a mesh, each triangle defines a normal (the cross product). It is important to note that a mesh isn't just a collection of independent faces. The faces are connected and this connectivity enforces a coherent orientation across the mesh.
pcl::PolygonMesh is an "half edge data structure". This means that every triangle face is defined by an ordered set of vertices, which defines the orientation:
order of vertices => order of cross product => well defined unambiguous normals
You can either use the normals from the mesh (nearest neighbor), or calculate a low resolution mesh and just use it to orient the cloud.
I need to check if the distance between two geographic point is less then N km. I'm trying to execute this query:
select st_distance(
ST_GeomFromText('point(45.764043 4.835658999999964)', 4326),
ST_GeomFromText('point(45.750371 5.053963)', 4326)
) < :n
But it doesn't work because:
So far the SRID property is just a dummy in MySQL, it is stored as part of a geometries meta data but all actual calculations ignore it and calculations are done assuming Euclidean (planar) geometry.
(https://mariadb.com/kb/en/mariadb/st_transform-missing/)
My goal is to convert this distance to the metric distance or to convert the N to the degrees.
How I can do it?
Maybe, you know a better solution?
P.S. I need a solution based on the spatial methods (or more better for the performance).
I don't think the "distance" function is available (yet) in SPATIAL. There is a regular FUNCTION in https://mariadb.com/kb/en/latitudelongitude-indexing/ that does the work. However, the args and output are scaled lat/lng (10000*degrees). The code could be altered to avoid the scaling, but it is needed in the context of that blog page.
I am trying to allow an entire graph (such as a collapsible dendrogram (http://mbostock.github.com/d3/talk/20111018/tree.html) to be moved in the visualization by using the centroid of the entire graph and do not know what the best course of action would be to treat this graph as a discrete node and to use the centroid coordinates for manipulation. How would the centroid of a graph be calculated, allowing for the graph to be dynamically sized to allow for new nodes, thereby recalculating the centroid?
It sounds like in your case an easier course of action would be to get the container of the graph (i.e. the SVG) and calculate its center point. Assuming that the margins of the graph you're drawing are not too big and/or unbalanced, the behaviour would be very similar to computing the centroid of the graph, but much easier to achieve.
The code would look something like this.
var centerx = svg.getBBox().x + svg.getBBox().width/2,
centery = svg.getBBox().y + svg.getBBox().height/2;
Perhaps this is helpful: http://graphstream-project.org/doc/Algorithms/Centroid_1.0/. I know you are asking in the context of D3.js. But there may be something to leverage from the centroid in GraphStream.