Forums - KDTrees for Image Matching

Hi everyone,

In my current application I have implemented a brute force descriptor matcher to get correspondences between 2 images, using fcvSumOfSquaredDiffs36xNs8. Since there are KDTrees supported within the new FastCV version I would like to implement a matcher using these KDTrees and kNN for better runtime performance.

I've implemented my first approach with kdtrees similar to the brute force approach, but my computed correspondences seem to be wrong (I don't get a correct homography, but I did get one with my brute force algorithm). What would be the correct way? As the code below shows, I'm creating a kdTree from the descriptors of the feature points of my target image. Then I loop over my reference image descriptors and query for the nearest neighbor within the kdTree. I'm only searching for 2 nearest neighbors (maxNN), since I only need the best match as correspondence and the second best match for a ratio test to reject outliers. I'm not sure how to set the other parameters, such as maxDist and maxChecks. Is my approach wrong? If yes, what would be the correct way and what would be good values for maxDist and maxChecks?

float32_t* invLenB = (float32_t*)fcvMemAlloc(state.numCorners*sizeof(float32_t), 16);
int8_t **descriptorsB = (int8_t **) fcvMemAlloc(state.numCorners * sizeof(int8_t *), 16);

for (uint32_t i = 0; i < state.numCorners; i++) {
	descriptorsB[i] = state.imageFeatureBuf[i].descriptor;
	invLenB[i] = (float)(1/sqrt((float)state.imageFeatureBuf[i].normSq));
}

fcvKDTreeDatas8f32* kdTree;
fcvKDTreeCreate36s8f32(*descriptorsB, invLenB, state.numCorners, &kdTree);

int maxNN = 2;
for(int k = 0; k < state.numCornersReferenceImg; k++){
	int32_t numFound;
	int32_t* indicesFound = (int32_t*)fcvMemAlloc(maxNN*sizeof(int32_t), 8);
	float32_t* distancesFound = (float32_t*)fcvMemAlloc(maxNN*sizeof(float32_t), 8);

	fcvFeatureType feature = state.referenceFeatureBuf[k];
	float invLen = (float)(1/sqrt((float)feature.normSq));

	fcvKDTreeQuery36s8f32(kdTree, feature.descriptor, invLen, maxNN, 0.1, 200, NULL, &numFound, indicesFound, distancesFound);

	if(numFound >0){
		int correspondenceIdx = indicesFound[0];
		fcvFeatureType correspondenceFeature = state.imageFeatureBuf[correspondenceIdx];
	}
}