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SDRReg.m
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SDRReg.m
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%SDRSAC
% M and B are input point cloud (can be dense)
function [out] = SDRReg(M, B, config)
add_dependencies;
%rng(2912670);
iter=0;
stop = false;
out.time = 0; out.iter = 0; out.maxInls = 0;
M_subsampled = subSamplePointCloud(M, config.NSubsample, 'random');
B_subsampled = subSamplePointCloud(B, config.NSubsample, 'random');
B_subsampled_Tree=KDTreeSearcher(B_subsampled');
%Now, compute the surface normals
normalM = computeNormal(M); normalB = computeNormal(B);
MTree = KDTreeSearcher(M'); BTree = KDTreeSearcher(B');
cMIdx = knnsearch(MTree, M_subsampled');
cBIdx = knnsearch(BTree, B_subsampled');
normalM_subsampled = normalM(:, cMIdx);
normalB_subsampled = normalB(:, cBIdx);
n = config.pointPerSample;
maxInls = 0;
% For large-span sampling
bb = boundingBox(M_subsampled);
maxDist = abs(bb(:,2) - bb(:,1));
config.pointDistance = config.pointDistance*min(maxDist);
% For parallel
PTM = cell( config.innerIter,1); PRT = cell( config.innerIter, 1);
PInls = zeros( config.innerIter,1); PTime = zeros(config.innerIter, 1);
% Start sampling
while (iter < config.maxIter&& ~stop)
tic;
idxBPool = []; idxM = [];
for sp = 1:config.kSample
CS = [];
while (isempty(CS))
disp('---------Subsample for congruent----------');
[idxMC, r1, r2, d1, d2, angle] = sampleCoplanarPoints(M_subsampled, idxM, config);
B_subsampled_for_congruent = B_subsampled(:, randsample(size(B_subsampled,2), min(config.SamplesForCongruent, size(B_subsampled,2))));
CS = findCongruentSet(B_subsampled_for_congruent, r1, r2, d1, d2, angle, config);
% close all; plotPointClouds(M_subsampled, M_subsampled(:, idxMC), 'b.', 'ro');
% hold on;
% plot_plane( M_subsampled(:, idxMC(1:3)));
% if (length(CS)>0)
% figure;
% plotPointClouds(B_subsampled, B_subsampled(:, CS(1,:)), 'b.', 'ro');
% hold on;
% plot_plane( B_subsampled(:, CS(1,1:3)));
% end
%
end
idxM = [idxM; idxMC];
idxBPool = [idxBPool; CS(:)];
end
% Now, a subset of M has been sampled and its congruent sets
idxBPool = unique(idxBPool);
M_congruent = M_subsampled(:, idxM);
normalM_congruent = normalM_subsampled(:, idxM);
B_congruent = B_subsampled_for_congruent(:, idxBPool);
normalB_congruent = normalB_subsampled(:, idxBPool);
m = M_congruent;
normalm = normalM_congruent;
nIter = config.innerIter;
% Generate samples for parfor
gIdx =zeros(nIter, n);
for it=1:nIter
gIdx(it,:) = randsample(size(B_congruent,2), n)';
%gIdx(it,:) = samplePoints(B_congruent, n, [], config);
end
if (size(idxBPool)<=n+1)
nIter = 1;
disp('idxBPool'); disp(size(idxBPool));
end
out.time = out.time+toc;
iter = iter + nIter;
parfor it = 1:nIter
tic;
idxB = gIdx(it,:);
b = B_congruent(:, idxB);
normalb = normalB_congruent(:, idxB);
[Rs,ts, ~, corrB] = sdpReg(m, b, normalm, normalb, config);
if (~isempty(corrB))
TM = Rs * M_subsampled + repmat(ts, 1, size(M_subsampled, 2));
%[Ricp, Ticp] = icp(B_subsampled, TM, 'Matching', 'kDtree', 'iter', 20);
%Ricp = eye(3); Ticp = zeros(3,1);
[Ricp, Ticp] = icp(B_subsampled, TM, 'Matching', 'kDtree', 'WorstRejection', 0.3, 'iter', 20);
%[Ricp, Ticp] = huber_icp(B, TM, 20, 5, 1);
%[Ricp, Ticp] = huber_icp(B_subsampled, TM);
%Ricp = Rs; Ticp = ts;
TMICP = Ricp*Rs*M_subsampled + repmat(Ricp*ts + Ticp, 1, size(M_subsampled, 2));
inls_icp = countCorrespondences(TMICP, B_subsampled_Tree, config.epsilon_sampled);
TMTree = KDTreeSearcher(TMICP');
inls_icp2 = countCorrespondences(B_subsampled, TMTree,config.epsilon_sampled);
inls_icp = min(inls_icp, inls_icp2);
PRT{it} = [Ricp*Rs Ricp*ts + Ticp];
%PRT{it} = [Rs ts];
PTM{it} = Ricp*Rs*M_subsampled + repmat(Ricp*ts + Ticp, 1, size(M_subsampled, 2));
PInls(it) = inls_icp;
end
PTime(it) = toc;
disp(PTime(it));
end
out.time = out.time + sum(PTime);
disp(PInls);
disp(['======CURRENT MAX = ' num2str(maxInls)]);
[nInls,idx] = max(PInls);
if (nInls > maxInls)
maxInls = nInls;
bestRT = PRT{idx};
out.bestR = bestRT(:,1:3); out.bestT = bestRT(:, 4);
TM = out.bestR*M + repmat(out.bestT, 1, size(M, 2));
%[Ricp, Ticp] = icp(B, TM, 'Matching', 'kDtree', 'WorstRejection', 0.2, 'iter', 50);
%[Ricp, Ticp] = huber_icp(B, TM);
% [Ricp, Ticp] = icp(B, TM);
% TMICP = Ricp * TM + Ticp;
%
TMTree = KDTreeSearcher(TM');
totalInls = countCorrespondences(TM, BTree, config.epsilon);
totalInls2 = countCorrespondences(B, TMTree, config.epsilon);
out.inls = maxInls;
out.totalIns = totalInls;
close all; plotPointClouds(B_subsampled, PTM{idx}, 'b.','r.');
figure;
plotPointClouds(B, TM, 'b.','r.');
disp('---MaxInls----'); disp(maxInls);
disp('---Total----'); disp(totalInls);
disp('---Total2----'); disp(totalInls2);
disp(out.bestR); disp(out.bestT);
out.cloudA = TM; out.cloudB = B;
end
T = computeStopping(0.99, maxInls./size(M,2));
if (iter>=T && iter >= config.minIterRequired)
stop = true;
end
end
end