function [solutions, values, results_stats] = h3d_evaluate_solutions_hausdorff(p, scan1, scan2, add_R,add_T, solutions, npoints, plausible_threshold)
	% function [solutions, values] = h3d_evaluate_solutions_hausdorff(p, scan1, scan2, add_R,add_T, solutions, npoints)
	% 
	% Evaluates the goodness of a list of solutions using Hausdorff distance.
	%
	% - solutions is a list of tuples {rotation, translation}
	% - npoints is how many random points to use from the second scan.
	%
	% - add_R and add_T are added to the points of the second scan
	
	
	% Sample npoints from the second scan

	scan2_important = h3d_scan_sample_points(scan2, npoints);
	sampled_points = {};
	for i=1:size(scan2_important.sampled_points,2)
		sampled_points{end+1} = scan2_important.sampled_points(1:3,i);
	end
	
	 h3d_display_clouds(scan2_important.sampled_points,eye(3),[0;0;0],scan2_important.sampled_points,eye(3),[0;0;0],1)
	
	
	max_obs = -1000;
	
	% For each solution
	for s=1:numel(solutions)
	
		R = solutions{s}{1};
		T = solutions{s}{2};
		
		% Each point can be one of these:
		
		num_out_of_field = 0;
		num_behind_something = 0;
		num_inconsistent = 0;
		num_plausible = 0;
		
		
		% For each sampled point
		for a=1:numel(sampled_points)
			p = sampled_points{a};
			
			% Coordinates in the first scan frame of reference
			% given the estimated R,T
			pw = R' * ( p - T);
			
			pw = add_R * pw + add_T;
			
			[visible, rho] = h3d_scan_whats_your_view(scan1, pw);
	
		    weight = norm(pw);
			
			if not(visible)
				num_out_of_field = num_out_of_field + weight;
				continue;
			end
			
			if abs(rho - norm(pw)) < plausible_threshold
				num_plausible = num_plausible + weight;
			else
				if rho < norm(pw)
					num_behind_something = num_behind_something + weight;
				else
					num_inconsistent = num_inconsistent + weight;
				end
			end	
		end
		
		values(s) = num_plausible;

		results_stats{s} = {num_out_of_field, num_behind_something, num_inconsistent, num_plausible};

		if values(s) > 0
			max_obs = values(s);
			fprintf('%5d/%d: Out %5.2f Behind %5.2f Incons %5.2f Plaus %5.2f   val %5.2f\n', s, numel(solutions),num_out_of_field, num_behind_something, num_inconsistent, num_plausible, values(s));			
		end
		
	end

	% sort the hypotheses
	[Y,I] = sort(values, 'descend');
	solutions = {solutions{I}};
	values = values(I);
	results_stats = {results_stats{I}};