Method for rendering 2D and 3D data within a 3D virtual environment
One variation of a method includes: accessing a 2D color image recorded by a 2D color camera and a 3D point cloud recorded by a 3D depth sensor at approximately a first time, the 2D color camera and the 3D depth sensor defining intersecting fields of view and facing outwardly from an autonomous vehicle; detecting a cluster of points in the 3D point cloud representing a continuous surface approximating a plane; isolating a cluster of color pixels in the 2D color image depicting the continuous surface; projecting the cluster of color pixels onto the plane to define a set of synthetic 3D color points in the 3D point cloud, the cluster of points and the set of synthetic 3D color points representing the continuous surface; and rendering points in the 3D point cloud and the set of synthetic 3D color points on a display.
1. A method for augmenting 3D depth map data with 2D color image data, the method comprising:
accessing a 3D point cloud captured using a 3D depth sensor of the vehicle;
accessing a concurrent 2D image captured using a 2D camera of a vehicle;
detecting a cluster of points in the 3D point cloud corresponding to a region;
identifying a cluster of pixels in the concurrent 2D image, the cluster of pixels depicting the region; and
generating an augmented 3D point cloud by augmenting the cluster of points in the 3D point cloud with a set of synthetic 3D points generated from the cluster of pixels, the set of synthetic 3D points being defined based on a projection of the cluster of pixels onto the 3D point cloud.
2. The method of claim 1 , wherein the set of synthetic 3D points corresponds to one or more gaps in the cluster of points.
3. The method of claim 1 , further comprising:
rendering the augmented 3D point cloud on a display.
4. The method of claim 1 , further comprising:
identifying one or more objects corresponding to the region using the augmented 3D point cloud.
5. The method of claim 1 , further comprising:
determining a transform between the 3D depth sensor and the 2D camera based on a relationship between the cluster of pixels and the cluster of points.
6. The method of claim 1 , further comprising:
aligning the 3D depth sensor and the 2D camera based on a relationship between the cluster of pixels and the cluster of points.
7. The method of claim 1 , wherein the augmented 3D point cloud has a point density proportional to an importance of the region.
8. One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
accessing a 3D point cloud captured using a 3D depth sensor of the vehicle;
accessing a concurrent 2D image captured using a 2D camera of a vehicle;
detecting a cluster of points in the 3D point cloud corresponding to a region;
identifying a cluster of pixels in the concurrent 2D image, the cluster of pixels depicting the region; and
generating an augmented 3D point cloud by augmenting the cluster of points in the 3D point cloud with a set of synthetic 3D points generated from the cluster of pixels, the set of synthetic 3D points being defined based on a projection of the cluster of pixels onto the 3D point cloud.
9. The one or more tangible non-transitory computer-readable storage media of claim 8 , wherein the set of synthetic 3D points corresponds to one or more gaps in the cluster of points.
10. The one or more tangible non-transitory computer-readable storage media of claim 8 , further comprising:
rendering the augmented 3D point cloud on a display.
11. The one or more tangible non-transitory computer-readable storage media of claim 8 , further comprising:
identifying one or more objects corresponding to the region using the augmented 3D point cloud.
12. The one or more tangible non-transitory computer-readable storage media of claim 8 , further comprising:
determining a transform between the 3D depth sensor and the 2D camera based on a relationship between the cluster of pixels and the cluster of points.
13. The one or more tangible non-transitory computer-readable storage media of claim 8 , further comprising:
aligning the 3D depth sensor and the 2D camera based on a relationship between the cluster of pixels and the cluster of points.
14. One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
defining a virtual environment corresponding a field around a vehicle;
accessing a 2D image captured using a 2D camera of the vehicle;
accessing a 3D point cloud captured using a 3D depth sensor of the vehicle;
rendering the 3D point cloud within the virtual environment;
defining a 2D image plane within the virtual environment;
projecting the 2D image onto the 2D image plane within the virtual environment; and
generating a fusion of the 2D image with the 3D point cloud within the virtual environment based on the projection of the 2D image onto the 2D image plane.
15. The one or more tangible non-transitory computer-readable storage media of claim 14 , wherein the 2D image is projected onto the 2D image plane based on a position of the 2D camera and a position of the 3D depth sensor.
16. The one or more tangible non-transitory computer-readable storage media of claim 14 , wherein an alignment of the 3D camera and the 3D depth sensor is determined based on the fusion of the 2D image with the 3D point cloud.
17. The one or more tangible non-transitory computer-readable storage media of claim 14 , wherein the 3D point cloud is rendered concurrently within the virtual environment with the 2D image being projected onto the 2D image plane.
18. The one or more tangible non-transitory computer-readable storage media of claim 14 , wherein the 3D point cloud is part of a series of 3D frames and the 2D image is part of a series of 2D images.