BLI_kdtree: refactor to support different numbers of dimensions

This moves logic into kdtree_impl.h which is included in a source file that defines the number of dimensions - so we can easily support different numbers of dimensions as needed (currently 3D and 4D are supported). Macro use isn't so nice but avoids a lot of duplicate code.
2019-03-18 11:22:48 +11:00
parent e72dc667c4
commit 0719d5fa0c
6 changed files with 256 additions and 105 deletions
--- a/source/blender/blenlib/intern/kdtree_impl.h
+++ b/source/blender/blenlib/intern/kdtree_impl.h
@@ -0,0 +1,912 @@
+/*
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License
+ * as published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software Foundation,
+ * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
+ */
+
+/** \file
+ * \ingroup bli
+ */
+
+#include "MEM_guardedalloc.h"
+
+#include "BLI_math.h"
+#include "BLI_kdtree_impl.h"
+#include "BLI_utildefines.h"
+#include "BLI_strict_flags.h"
+
+#define _CONCAT_AUX(MACRO_ARG1, MACRO_ARG2) MACRO_ARG1 ## MACRO_ARG2
+#define _CONCAT(MACRO_ARG1, MACRO_ARG2) _CONCAT_AUX(MACRO_ARG1, MACRO_ARG2)
+#define BLI_kdtree_nd_(id) _CONCAT(KDTREE_PREFIX_ID, _##id)
+
+typedef struct KDTreeNode_head {
+	uint left, right;
+	float co[KD_DIMS];
+	int index;
+} KDTreeNode_head;
+
+typedef struct KDTreeNode {
+	uint left, right;
+	float co[KD_DIMS];
+	int index;
+	uint d;  /* range is only (0..KD_DIMS - 1) */
+} KDTreeNode;
+
+struct KDTree {
+	KDTreeNode *nodes;
+	uint nodes_len;
+	uint root;
+#ifdef DEBUG
+	bool is_balanced;  /* ensure we call balance first */
+	uint nodes_len_capacity;   /* max size of the tree */
+#endif
+};
+
+#define KD_STACK_INIT 100      /* initial size for array (on the stack) */
+#define KD_NEAR_ALLOC_INC 100  /* alloc increment for collecting nearest */
+#define KD_FOUND_ALLOC_INC 50  /* alloc increment for collecting nearest */
+
+#define KD_NODE_UNSET ((uint)-1)
+
+/** When set we know all values are unbalanced, otherwise clear them when re-balancing: see T62210. */
+#define KD_NODE_ROOT_IS_INIT ((uint)-2)
+
+/* -------------------------------------------------------------------- */
+/** \name Local Math API
+ * \{ */
+
+static void copy_vn_vn(float v0[KD_DIMS], const float v1[KD_DIMS])
+{
+	for (uint j = 0; j < KD_DIMS; j++) {
+		v0[j] = v1[j];
+	}
+}
+
+static float len_squared_vnvn(const float v0[KD_DIMS], const float v1[KD_DIMS])
+{
+	float d = 0.0f;
+	for (uint j = 0; j < KD_DIMS; j++) {
+		d += SQUARE(v0[j] - v1[j]);
+	}
+	return d;
+}
+
+static float len_squared_vnvn_cb(const float co_kdtree[KD_DIMS], const float co_search[KD_DIMS], const void *UNUSED(user_data))
+{
+	return len_squared_vnvn(co_kdtree, co_search);
+}
+
+/** \} */
+
+/**
+ * Creates or free a kdtree
+ */
+KDTree *BLI_kdtree_nd_(new)(uint nodes_len_capacity)
+{
+	KDTree *tree;
+
+	tree = MEM_mallocN(sizeof(KDTree), "KDTree");
+	tree->nodes = MEM_mallocN(sizeof(KDTreeNode) * nodes_len_capacity, "KDTreeNode");
+	tree->nodes_len = 0;
+	tree->root = KD_NODE_ROOT_IS_INIT;
+
+#ifdef DEBUG
+	tree->is_balanced = false;
+	tree->nodes_len_capacity = nodes_len_capacity;
+#endif
+
+	return tree;
+}
+
+void BLI_kdtree_nd_(free)(KDTree *tree)
+{
+	if (tree) {
+		MEM_freeN(tree->nodes);
+		MEM_freeN(tree);
+	}
+}
+
+/**
+ * Construction: first insert points, then call balance. Normal is optional.
+ */
+void BLI_kdtree_nd_(insert)(KDTree *tree, int index, const float co[KD_DIMS])
+{
+	KDTreeNode *node = &tree->nodes[tree->nodes_len++];
+
+#ifdef DEBUG
+	BLI_assert(tree->nodes_len <= tree->nodes_len_capacity);
+#endif
+
+	/* note, array isn't calloc'd,
+	 * need to initialize all struct members */
+
+	node->left = node->right = KD_NODE_UNSET;
+	copy_vn_vn(node->co, co);
+	node->index = index;
+	node->d = 0;
+
+#ifdef DEBUG
+	tree->is_balanced = false;
+#endif
+}
+
+static uint kdtree_balance(KDTreeNode *nodes, uint nodes_len, uint axis, const uint ofs)
+{
+	KDTreeNode *node;
+	float co;
+	uint left, right, median, i, j;
+
+	if (nodes_len <= 0) {
+		return KD_NODE_UNSET;
+	}
+	else if (nodes_len == 1) {
+		return 0 + ofs;
+	}
+
+	/* quicksort style sorting around median */
+	left = 0;
+	right = nodes_len - 1;
+	median = nodes_len / 2;
+
+	while (right > left) {
+		co = nodes[right].co[axis];
+		i = left - 1;
+		j = right;
+
+		while (1) {
+			while (nodes[++i].co[axis] < co) { /* pass */ }
+			while (nodes[--j].co[axis] > co && j > left) { /* pass */ }
+
+			if (i >= j) {
+				break;
+			}
+
+			SWAP(KDTreeNode_head, *(KDTreeNode_head *)&nodes[i], *(KDTreeNode_head *)&nodes[j]);
+		}
+
+		SWAP(KDTreeNode_head, *(KDTreeNode_head *)&nodes[i], *(KDTreeNode_head *)&nodes[right]);
+		if (i >= median) {
+			right = i - 1;
+		}
+		if (i <= median) {
+			left = i + 1;
+		}
+	}
+
+	/* set node and sort subnodes */
+	node = &nodes[median];
+	node->d = axis;
+	axis = (axis + 1) % KD_DIMS;
+	node->left = kdtree_balance(nodes, median, axis, ofs);
+	node->right = kdtree_balance(nodes + median + 1, (nodes_len - (median + 1)), axis, (median + 1) + ofs);
+
+	return median + ofs;
+}
+
+void BLI_kdtree_nd_(balance)(KDTree *tree)
+{
+	if (tree->root != KD_NODE_ROOT_IS_INIT) {
+		for (uint i = 0; i < tree->nodes_len; i++) {
+			tree->nodes[i].left = KD_NODE_UNSET;
+			tree->nodes[i].right = KD_NODE_UNSET;
+		}
+	}
+
+	tree->root = kdtree_balance(tree->nodes, tree->nodes_len, 0, 0);
+
+#ifdef DEBUG
+	tree->is_balanced = true;
+#endif
+}
+
+static uint *realloc_nodes(uint *stack, uint *stack_len_capacity, const bool is_alloc)
+{
+	uint *stack_new = MEM_mallocN((*stack_len_capacity + KD_NEAR_ALLOC_INC) * sizeof(uint), "KDTree.treestack");
+	memcpy(stack_new, stack, *stack_len_capacity * sizeof(uint));
+	// memset(stack_new + *stack_len_capacity, 0, sizeof(uint) * KD_NEAR_ALLOC_INC);
+	if (is_alloc) {
+		MEM_freeN(stack);
+	}
+	*stack_len_capacity += KD_NEAR_ALLOC_INC;
+	return stack_new;
+}
+
+/**
+ * Find nearest returns index, and -1 if no node is found.
+ */
+int BLI_kdtree_nd_(find_nearest)(
+        const KDTree *tree, const float co[KD_DIMS],
+        KDTreeNearest *r_nearest)
+{
+	const KDTreeNode *nodes = tree->nodes;
+	const KDTreeNode *root, *min_node;
+	uint *stack, stack_default[KD_STACK_INIT];
+	float min_dist, cur_dist;
+	uint stack_len_capacity, cur = 0;
+
+#ifdef DEBUG
+	BLI_assert(tree->is_balanced == true);
+#endif
+
+	if (UNLIKELY(tree->root == KD_NODE_UNSET)) {
+		return -1;
+	}
+
+	stack = stack_default;
+	stack_len_capacity = KD_STACK_INIT;
+
+	root = &nodes[tree->root];
+	min_node = root;
+	min_dist = len_squared_vnvn(root->co, co);
+
+	if (co[root->d] < root->co[root->d]) {
+		if (root->right != KD_NODE_UNSET) {
+			stack[cur++] = root->right;
+		}
+		if (root->left != KD_NODE_UNSET) {
+			stack[cur++] = root->left;
+		}
+	}
+	else {
+		if (root->left != KD_NODE_UNSET) {
+			stack[cur++] = root->left;
+		}
+		if (root->right != KD_NODE_UNSET) {
+			stack[cur++] = root->right;
+		}
+	}
+
+	while (cur--) {
+		const KDTreeNode *node = &nodes[stack[cur]];
+
+		cur_dist = node->co[node->d] - co[node->d];
+
+		if (cur_dist < 0.0f) {
+			cur_dist = -cur_dist * cur_dist;
+
+			if (-cur_dist < min_dist) {
+				cur_dist = len_squared_vnvn(node->co, co);
+				if (cur_dist < min_dist) {
+					min_dist = cur_dist;
+					min_node = node;
+				}
+				if (node->left != KD_NODE_UNSET) {
+					stack[cur++] = node->left;
+				}
+			}
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+		else {
+			cur_dist = cur_dist * cur_dist;
+
+			if (cur_dist < min_dist) {
+				cur_dist = len_squared_vnvn(node->co, co);
+				if (cur_dist < min_dist) {
+					min_dist = cur_dist;
+					min_node = node;
+				}
+				if (node->right != KD_NODE_UNSET) {
+					stack[cur++] = node->right;
+				}
+			}
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+		}
+		if (UNLIKELY(cur + KD_DIMS > stack_len_capacity)) {
+			stack = realloc_nodes(stack, &stack_len_capacity, stack_default != stack);
+		}
+	}
+
+	if (r_nearest) {
+		r_nearest->index = min_node->index;
+		r_nearest->dist = sqrtf(min_dist);
+		copy_vn_vn(r_nearest->co, min_node->co);
+	}
+
+	if (stack != stack_default) {
+		MEM_freeN(stack);
+	}
+
+	return min_node->index;
+}
+
+
+/**
+ * A version of #BLI_kdtree_find_nearest which runs a callback
+ * to filter out values.
+ *
+ * \param filter_cb: Filter find results,
+ * Return codes: (1: accept, 0: skip, -1: immediate exit).
+ */
+int BLI_kdtree_nd_(find_nearest_cb)(
+        const KDTree *tree, const float co[KD_DIMS],
+        int (*filter_cb)(void *user_data, int index, const float co[KD_DIMS], float dist_sq), void *user_data,
+        KDTreeNearest *r_nearest)
+{
+	const KDTreeNode *nodes = tree->nodes;
+	const KDTreeNode *min_node = NULL;
+
+	uint *stack, stack_default[KD_STACK_INIT];
+	float min_dist = FLT_MAX, cur_dist;
+	uint stack_len_capacity, cur = 0;
+
+#ifdef DEBUG
+	BLI_assert(tree->is_balanced == true);
+#endif
+
+	if (UNLIKELY(tree->root == KD_NODE_UNSET)) {
+		return -1;
+	}
+
+	stack = stack_default;
+	stack_len_capacity = ARRAY_SIZE(stack_default);
+
+#define NODE_TEST_NEAREST(node) \
+{ \
+	const float dist_sq = len_squared_vnvn((node)->co, co); \
+	if (dist_sq < min_dist) { \
+		const int result = filter_cb(user_data, (node)->index, (node)->co, dist_sq); \
+		if (result == 1) { \
+			min_dist = dist_sq; \
+			min_node = node; \
+		} \
+		else if (result == 0) { \
+			/* pass */ \
+		} \
+		else { \
+			BLI_assert(result == -1); \
+			goto finally; \
+		} \
+	} \
+} ((void)0)
+
+	stack[cur++] = tree->root;
+
+	while (cur--) {
+		const KDTreeNode *node = &nodes[stack[cur]];
+
+		cur_dist = node->co[node->d] - co[node->d];
+
+		if (cur_dist < 0.0f) {
+			cur_dist = -cur_dist * cur_dist;
+
+			if (-cur_dist < min_dist) {
+				NODE_TEST_NEAREST(node);
+
+				if (node->left != KD_NODE_UNSET) {
+					stack[cur++] = node->left;
+				}
+			}
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+		else {
+			cur_dist = cur_dist * cur_dist;
+
+			if (cur_dist < min_dist) {
+				NODE_TEST_NEAREST(node);
+
+				if (node->right != KD_NODE_UNSET) {
+					stack[cur++] = node->right;
+				}
+			}
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+		}
+		if (UNLIKELY(cur + KD_DIMS > stack_len_capacity)) {
+			stack = realloc_nodes(stack, &stack_len_capacity, stack_default != stack);
+		}
+	}
+
+#undef NODE_TEST_NEAREST
+
+
+finally:
+	if (stack != stack_default) {
+		MEM_freeN(stack);
+	}
+
+	if (min_node) {
+		if (r_nearest) {
+			r_nearest->index = min_node->index;
+			r_nearest->dist = sqrtf(min_dist);
+			copy_vn_vn(r_nearest->co, min_node->co);
+		}
+
+		return min_node->index;
+	}
+	else {
+		return -1;
+	}
+}
+
+static void nearest_ordered_insert(
+        KDTreeNearest *nearest, uint *nearest_len, const uint nearest_len_capacity,
+        const int index, const float dist, const float co[KD_DIMS])
+{
+	uint i;
+
+	if (*nearest_len < nearest_len_capacity) {
+		(*nearest_len)++;
+	}
+
+	for (i = *nearest_len - 1; i > 0; i--) {
+		if (dist >= nearest[i - 1].dist) {
+			break;
+		}
+		else {
+			nearest[i] = nearest[i - 1];
+		}
+	}
+
+	nearest[i].index = index;
+	nearest[i].dist = dist;
+	copy_vn_vn(nearest[i].co, co);
+}
+
+/**
+ * Find \a nearest_len_capacity nearest returns number of points found, with results in nearest.
+ *
+ * \param r_nearest: An array of nearest, sized at least \a nearest_len_capacity.
+ */
+int BLI_kdtree_nd_(find_nearest_n_with_len_squared_cb)(
+        const KDTree *tree, const float co[KD_DIMS],
+        KDTreeNearest r_nearest[],
+        const uint nearest_len_capacity,
+        float (*len_sq_fn)(const float co_search[KD_DIMS], const float co_test[KD_DIMS], const void *user_data),
+        const void *user_data)
+{
+	const KDTreeNode *nodes = tree->nodes;
+	const KDTreeNode *root;
+	uint *stack, stack_default[KD_STACK_INIT];
+	float cur_dist;
+	uint stack_len_capacity, cur = 0;
+	uint i, nearest_len = 0;
+
+#ifdef DEBUG
+	BLI_assert(tree->is_balanced == true);
+#endif
+
+	if (UNLIKELY((tree->root == KD_NODE_UNSET) || nearest_len_capacity == 0)) {
+		return 0;
+	}
+
+	if (len_sq_fn == NULL) {
+		len_sq_fn = len_squared_vnvn_cb;
+		BLI_assert(user_data == NULL);
+	}
+
+	stack = stack_default;
+	stack_len_capacity = ARRAY_SIZE(stack_default);
+
+	root = &nodes[tree->root];
+
+	cur_dist = len_sq_fn(co, root->co, user_data);
+	nearest_ordered_insert(r_nearest, &nearest_len, nearest_len_capacity, root->index, cur_dist, root->co);
+
+	if (co[root->d] < root->co[root->d]) {
+		if (root->right != KD_NODE_UNSET) {
+			stack[cur++] = root->right;
+		}
+		if (root->left != KD_NODE_UNSET) {
+			stack[cur++] = root->left;
+		}
+	}
+	else {
+		if (root->left != KD_NODE_UNSET) {
+			stack[cur++] = root->left;
+		}
+		if (root->right != KD_NODE_UNSET) {
+			stack[cur++] = root->right;
+		}
+	}
+
+	while (cur--) {
+		const KDTreeNode *node = &nodes[stack[cur]];
+
+		cur_dist = node->co[node->d] - co[node->d];
+
+		if (cur_dist < 0.0f) {
+			cur_dist = -cur_dist * cur_dist;
+
+			if (nearest_len < nearest_len_capacity || -cur_dist < r_nearest[nearest_len - 1].dist) {
+				cur_dist = len_sq_fn(co, node->co, user_data);
+
+				if (nearest_len < nearest_len_capacity || cur_dist < r_nearest[nearest_len - 1].dist) {
+					nearest_ordered_insert(r_nearest, &nearest_len, nearest_len_capacity, node->index, cur_dist, node->co);
+				}
+
+				if (node->left != KD_NODE_UNSET) {
+					stack[cur++] = node->left;
+				}
+			}
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+		else {
+			cur_dist = cur_dist * cur_dist;
+
+			if (nearest_len < nearest_len_capacity || cur_dist < r_nearest[nearest_len - 1].dist) {
+				cur_dist = len_sq_fn(co, node->co, user_data);
+				if (nearest_len < nearest_len_capacity || cur_dist < r_nearest[nearest_len - 1].dist) {
+					nearest_ordered_insert(r_nearest, &nearest_len, nearest_len_capacity, node->index, cur_dist, node->co);
+				}
+
+				if (node->right != KD_NODE_UNSET) {
+					stack[cur++] = node->right;
+				}
+			}
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+		}
+		if (UNLIKELY(cur + KD_DIMS > stack_len_capacity)) {
+			stack = realloc_nodes(stack, &stack_len_capacity, stack_default != stack);
+		}
+	}
+
+	for (i = 0; i < nearest_len; i++) {
+		r_nearest[i].dist = sqrtf(r_nearest[i].dist);
+	}
+
+	if (stack != stack_default) {
+		MEM_freeN(stack);
+	}
+
+	return (int)nearest_len;
+}
+
+int BLI_kdtree_nd_(find_nearest_n)(
+        const KDTree *tree, const float co[KD_DIMS],
+        KDTreeNearest r_nearest[],
+        const uint nearest_len_capacity)
+{
+	return BLI_kdtree_nd_(find_nearest_n_with_len_squared_cb)(
+	        tree, co, r_nearest, nearest_len_capacity,
+	        NULL, NULL);
+}
+
+static int nearest_cmp_dist(const void *a, const void *b)
+{
+	const KDTreeNearest *kda = a;
+	const KDTreeNearest *kdb = b;
+
+	if (kda->dist < kdb->dist) {
+		return -1;
+	}
+	else if (kda->dist > kdb->dist) {
+		return 1;
+	}
+	else {
+		return 0;
+	}
+}
+static void nearest_add_in_range(
+        KDTreeNearest **r_nearest,
+        uint  nearest_index,
+        uint *nearest_len_capacity,
+        const int index, const float dist, const float co[KD_DIMS])
+{
+	KDTreeNearest *to;
+
+	if (UNLIKELY(nearest_index >= *nearest_len_capacity)) {
+		*r_nearest = MEM_reallocN_id(
+		        *r_nearest,
+		        (*nearest_len_capacity += KD_FOUND_ALLOC_INC) * sizeof(KDTreeNode),
+		        __func__);
+	}
+
+	to = (*r_nearest) + nearest_index;
+
+	to->index = index;
+	to->dist = sqrtf(dist);
+	copy_vn_vn(to->co, co);
+}
+
+/**
+ * Range search returns number of points nearest_len, with results in nearest
+ *
+ * \param r_nearest: Allocated array of nearest nearest_len (caller is responsible for freeing).
+ */
+int BLI_kdtree_nd_(range_search_with_len_squared_cb)(
+        const KDTree *tree, const float co[KD_DIMS],
+        KDTreeNearest **r_nearest, const float range,
+        float (*len_sq_fn)(const float co_search[KD_DIMS], const float co_test[KD_DIMS], const void *user_data),
+        const void *user_data)
+{
+	const KDTreeNode *nodes = tree->nodes;
+	uint *stack, stack_default[KD_STACK_INIT];
+	KDTreeNearest *nearest = NULL;
+	const float range_sq = range * range;
+	float dist_sq;
+	uint stack_len_capacity, cur = 0;
+	uint nearest_len = 0, nearest_len_capacity = 0;
+
+#ifdef DEBUG
+	BLI_assert(tree->is_balanced == true);
+#endif
+
+	if (UNLIKELY(tree->root == KD_NODE_UNSET)) {
+		return 0;
+	}
+
+	if (len_sq_fn == NULL) {
+		len_sq_fn = len_squared_vnvn_cb;
+		BLI_assert(user_data == NULL);
+	}
+
+	stack = stack_default;
+	stack_len_capacity = ARRAY_SIZE(stack_default);
+
+	stack[cur++] = tree->root;
+
+	while (cur--) {
+		const KDTreeNode *node = &nodes[stack[cur]];
+
+		if (co[node->d] + range < node->co[node->d]) {
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+		}
+		else if (co[node->d] - range > node->co[node->d]) {
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+		else {
+			dist_sq = len_sq_fn(co, node->co, user_data);
+			if (dist_sq <= range_sq) {
+				nearest_add_in_range(&nearest, nearest_len++, &nearest_len_capacity, node->index, dist_sq, node->co);
+			}
+
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+
+		if (UNLIKELY(cur + KD_DIMS > stack_len_capacity)) {
+			stack = realloc_nodes(stack, &stack_len_capacity, stack_default != stack);
+		}
+	}
+
+	if (stack != stack_default) {
+		MEM_freeN(stack);
+	}
+
+	if (nearest_len) {
+		qsort(nearest, nearest_len, sizeof(KDTreeNearest), nearest_cmp_dist);
+	}
+
+	*r_nearest = nearest;
+
+	return (int)nearest_len;
+}
+
+int BLI_kdtree_nd_(range_search)(
+        const KDTree *tree, const float co[KD_DIMS],
+        KDTreeNearest **r_nearest, const float range)
+{
+	return BLI_kdtree_nd_(range_search_with_len_squared_cb)(
+	        tree, co, r_nearest, range,
+	        NULL, NULL);
+}
+
+/**
+ * A version of #BLI_kdtree_range_search which runs a callback
+ * instead of allocating an array.
+ *
+ * \param search_cb: Called for every node found in \a range, false return value performs an early exit.
+ *
+ * \note the order of calls isn't sorted based on distance.
+ */
+void BLI_kdtree_nd_(range_search_cb)(
+        const KDTree *tree, const float co[KD_DIMS], float range,
+        bool (*search_cb)(void *user_data, int index, const float co[KD_DIMS], float dist_sq), void *user_data)
+{
+	const KDTreeNode *nodes = tree->nodes;
+
+	uint *stack, stack_default[KD_STACK_INIT];
+	float range_sq = range * range, dist_sq;
+	uint stack_len_capacity, cur = 0;
+
+#ifdef DEBUG
+	BLI_assert(tree->is_balanced == true);
+#endif
+
+	if (UNLIKELY(tree->root == KD_NODE_UNSET)) {
+		return;
+	}
+
+	stack = stack_default;
+	stack_len_capacity = ARRAY_SIZE(stack_default);
+
+	stack[cur++] = tree->root;
+
+	while (cur--) {
+		const KDTreeNode *node = &nodes[stack[cur]];
+
+		if (co[node->d] + range < node->co[node->d]) {
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+		}
+		else if (co[node->d] - range > node->co[node->d]) {
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+		else {
+			dist_sq = len_squared_vnvn(node->co, co);
+			if (dist_sq <= range_sq) {
+				if (search_cb(user_data, node->index, node->co, dist_sq) == false) {
+					goto finally;
+				}
+			}
+
+			if (node->left != KD_NODE_UNSET) {
+				stack[cur++] = node->left;
+			}
+			if (node->right != KD_NODE_UNSET) {
+				stack[cur++] = node->right;
+			}
+		}
+
+		if (UNLIKELY(cur + KD_DIMS > stack_len_capacity)) {
+			stack = realloc_nodes(stack, &stack_len_capacity, stack_default != stack);
+		}
+	}
+
+finally:
+	if (stack != stack_default) {
+		MEM_freeN(stack);
+	}
+}
+
+/**
+ * Use when we want to loop over nodes ordered by index.
+ * Requires indices to be aligned with nodes.
+ */
+static uint *kdtree_order(const KDTree *tree)
+{
+	const KDTreeNode *nodes = tree->nodes;
+	uint *order = MEM_mallocN(sizeof(uint) * tree->nodes_len, __func__);
+	for (uint i = 0; i < tree->nodes_len; i++) {
+		order[nodes[i].index] = i;
+	}
+	return order;
+}
+
+/* -------------------------------------------------------------------- */
+/** \name BLI_kdtree_calc_duplicates_fast
+ * \{ */
+
+struct DeDuplicateParams {
+	/* Static */
+	const KDTreeNode *nodes;
+	float range;
+	float range_sq;
+	int *duplicates;
+	int *duplicates_found;
+
+	/* Per Search */
+	float search_co[KD_DIMS];
+	int search;
+};
+
+static void deduplicate_recursive(const struct DeDuplicateParams *p, uint i)
+{
+	const KDTreeNode *node = &p->nodes[i];
+	if (p->search_co[node->d] + p->range <= node->co[node->d]) {
+		if (node->left != KD_NODE_UNSET) {
+			deduplicate_recursive(p, node->left);
+		}
+	}
+	else if (p->search_co[node->d] - p->range >= node->co[node->d]) {
+		if (node->right != KD_NODE_UNSET) {
+			deduplicate_recursive(p, node->right);
+		}
+	}
+	else {
+		if ((p->search != node->index) && (p->duplicates[node->index] == -1)) {
+			if (len_squared_vnvn(node->co, p->search_co) <= p->range_sq) {
+				p->duplicates[node->index] = (int)p->search;
+				*p->duplicates_found += 1;
+			}
+		}
+		if (node->left != KD_NODE_UNSET) {
+			deduplicate_recursive(p, node->left);
+		}
+		if (node->right != KD_NODE_UNSET) {
+			deduplicate_recursive(p, node->right);
+		}
+	}
+}
+
+/**
+ * Find duplicate points in \a range.
+ * Favors speed over quality since it doesn't find the best target vertex for merging.
+ * Nodes are looped over, duplicates are added when found.
+ * Nevertheless results are predictable.
+ *
+ * \param range: Coordinates in this range are candidates to be merged.
+ * \param use_index_order: Loop over the coordinates ordered by #KDTreeNode.index
+ * At the expense of some performance, this ensures the layout of the tree doesn't influence
+ * the iteration order.
+ * \param duplicates: An array of int's the length of #KDTree.nodes_len
+ * Values initialized to -1 are candidates to me merged.
+ * Setting the index to it's own position in the array prevents it from being touched,
+ * although it can still be used as a target.
+ * \returns The number of merges found (includes any merges already in the \a duplicates array).
+ *
+ * \note Merging is always a single step (target indices wont be marked for merging).
+ */
+int BLI_kdtree_nd_(calc_duplicates_fast)(
+        const KDTree *tree, const float range, bool use_index_order,
+        int *duplicates)
+{
+	int found = 0;
+	struct DeDuplicateParams p = {
+		.nodes = tree->nodes,
+		.range = range,
+		.range_sq = SQUARE(range),
+		.duplicates = duplicates,
+		.duplicates_found = &found,
+	};
+
+	if (use_index_order) {
+		uint *order = kdtree_order(tree);
+		for (uint i = 0; i < tree->nodes_len; i++) {
+			const uint node_index = order[i];
+			const int index = (int)i;
+			if (ELEM(duplicates[index], -1, index)) {
+				p.search = index;
+				copy_vn_vn(p.search_co, tree->nodes[node_index].co);
+				int found_prev = found;
+				deduplicate_recursive(&p, tree->root);
+				if (found != found_prev) {
+					/* Prevent chains of doubles. */
+					duplicates[index] = index;
+				}
+			}
+		}
+		MEM_freeN(order);
+	}
+	else {
+		for (uint i = 0; i < tree->nodes_len; i++) {
+			const uint node_index = i;
+			const int index = p.nodes[node_index].index;
+			if (ELEM(duplicates[index], -1, index)) {
+				p.search = index;
+				copy_vn_vn(p.search_co, tree->nodes[node_index].co);
+				int found_prev = found;
+				deduplicate_recursive(&p, tree->root);
+				if (found != found_prev) {
+					/* Prevent chains of doubles. */
+					duplicates[index] = index;
+				}
+			}
+		}
+	}
+	return found;
+}
+
+/** \} */