Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

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Not AvailablePoor understanding of the genetic and molecular basis of heat tolerance component traits is a major bottleneck in designing heat tolerant wheat cultivars. The impact of terminal heat stress is generally reported in the case of late sown wheat. In this study, our aim was to identify genomic regions for various agronomic traits under late sown conditions by using genome-wide association approach. An association mapping panel of 205 wheat accessions was evaluated under late sown conditions at three different locations in India. Genotyping of the association panel revealed 15,886 SNPs, out of which 11,911 SNPs with exact physical locations on the wheat reference genome were used in association analysis. A total of 69 QTLs (10 significantly associated and 59 suggestive) were identified for ten different traits including productive tiller number (17), grain yield (14), plant height (12), grain filling rate (6), grain filling duration (5), days to physiological maturity (4), grain number (3), thousand grain weight (3), harvest index (3), and biomass (2). Out of these associated QTLs, 17 were novel for traits, namely PTL (3), GY (2), GFR (6), HI (3) and GNM (3). Moreover, five consistent QTLs across environments were identified for GY (4) and TGW (1). Also, 11 multi-trait SNPs and three hot spot regions on Chr1Ds, Chr2BS, Chr2DS harboring many QTLs for many traits were identified. In addition, identification of heat tolerant germplasm lines based on favorable alleles HD2888, IC611071, IC611273, IC75240, IC321906, IC416188, and J31-170 would facilitate their targeted introgression into popular wheat cultivars. The significantly associated QTLs identified in the present study can be further validated to identify robust markers for utilization in marker-assisted selection (MAS) for development of heat tolerant wheat cultivars.Not Availabl