▁based ▁on ▁a ▁fast ▁H ough - transform ▁voting ▁strategy ▁for ▁plan ar ▁regions , ▁inspired ▁by ▁the ▁K ernel - based ▁H ough ▁transform ▁( K HT ). ▁This ▁ 3 D ▁K ernel - based ▁H ough ▁transform ▁( 3 DK HT ) ▁uses ▁a ▁fast ▁and ▁robust ▁algorithm ▁to ▁segment ▁clusters ▁of ▁approximately ▁co - plan ar ▁samples , ▁and ▁cast s ▁votes ▁for ▁individual ▁clusters ▁( inst ead ▁of ▁for ▁individual ▁samples ) ▁on ▁a ▁() ▁ ▁spher ical ▁accum ulator ▁using ▁a ▁t riv ari ate ▁Gaussian ▁kernel . ▁The ▁approach ▁is ▁several ▁orders ▁of ▁magnitude ▁faster ▁than ▁existing ▁( non - det erm in istic ) ▁techniques ▁for ▁plane ▁detection ▁in ▁point ▁clouds , ▁such ▁as ▁R HT ▁and ▁R AN S AC , ▁and ▁scales ▁better ▁with ▁the ▁size ▁of ▁the ▁datasets . ▁It ▁can ▁be ▁used ▁with ▁any ▁application ▁that ▁requires ▁fast ▁detection ▁of ▁plan ar ▁features ▁on ▁large ▁datasets . ▁ ▁H ough ▁transform ▁of ▁curves , ▁and ▁its ▁general ization ▁for ▁analyt ical ▁and ▁non - anal yt ical ▁shapes ▁Although ▁the ▁version ▁of ▁the ▁transform ▁described ▁above ▁applies ▁only ▁to ▁finding ▁straight ▁lines , ▁a ▁similar ▁transform ▁can ▁be ▁used ▁for ▁finding ▁any ▁shape ▁which ▁can ▁be ▁represented ▁by ▁a ▁set ▁of ▁parameters . ▁ ▁A ▁circle , ▁for ▁instance , ▁can ▁be ▁transformed ▁into ▁a ▁set ▁of ▁three ▁parameters , ▁representing ▁its ▁center ▁and ▁radius , ▁so ▁that ▁the ▁H ough ▁space ▁becomes ▁three ▁dimensional . ▁ ▁Ar bitr ary ▁elli