REU Project 4D Efficient Real-Time Graph Based Temporal Point Cloud Segmentation Steven Hickson.

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Presentation transcript:

REU Project 4D Efficient Real-Time Graph Based Temporal Point Cloud Segmentation Steven Hickson

Constructing Point Clouds Construction of Point Clouds using multiple view geometry and calibration parameters.

Graph Based Segmentation Theory... each pixel is a node in the graph Graph G=(V,E) verticesedges Given a graph G=(V,E), the minimum spanning tree is a set of edges such that resulting graph is a tree the sum of all the edge weights is minimal Based of Pedro Felzenszwalb’s Efficient Graph Based Segmentation

Improvements Take Graph in 3D but to save time and memory, do two 2D segmentations (one with depth, one with color). Normalized, combined edge weights can cause problems. Implement multiple edge weights in order to be accurate. Planar Segmentation with depth, than fine inner segmentation with color. Include temporal information with optical flow, either by following the flow or adding a third weight Increase the Graph to 4D for optical flow (two 3D segmentations) Create minimum segment size specifications

Color Only Results

Depth Only Results

Current Results

Next Temporal information and optical flow. Overlapping time segments for graph merging. CUDA for sorting and constructing the graph to reduce time (will have to be careful considering the large amount of memory required)