请问有基于GPU并利用taichi实现linear bvh的例子嘛
这个不是用taichi做的吧，有没有用taichi做的例子呢
我实现了一版（由于整个项目的代码还有点问题所以没有开源，不过bvh的代码是能通过单元测试的）
from utils import *
from scene import Scene
from sort import *
BvhNode = ti.types.struct(bound=Bound, left=ti.i32, right=ti.i32, primitive=ti.i32, parent=ti.i32)
@ti.func
def is_leaf(node: BvhNode):
return node.primitive != -1
@ti.func
def left_3_shift(x: ti.u32):
# from pbrt
if x == (1 << 10):
x -= 1
x = (x | (x << 16)) & 0b00000011000000000000000011111111
# x = ---- --98 ---- ---- ---- ---- 7654 3210
x = (x | (x << 8)) & 0b00000011000000001111000000001111
# x = ---- --98 ---- ---- 7654 ---- ---- 3210
x = (x | (x << 4)) & 0b00000011000011000011000011000011
# x = ---- --98 ---- 76-- --54 ---- 32-- --10
x = (x | (x << 2)) & 0b00001001001001001001001001001001
# x = ---- 9--8 --7- -6-- 5--4 --3- -2-- 1--0
return x
@ti.func
def generate_morton_code(c: float3, ext: Bound):
offset = c - ext.min
total_ext = ext.max - ext.min
x = offset * 1024 / total_ext
return left_3_shift(ti.floor(x[0], dtype=ti.i32)) | left_3_shift(ti.math.floor(x[1], dtype=ti.i32)) << 1 \
| left_3_shift(ti.math.floor(x[2], dtype=ti.i32)) << 2
@ti.data_oriented
class Bvh:
def __init__(self, scene_ext, primitive_count, primitives, sorter="bi_gpu"):
self.primitives = primitives
self.internal_node_count = primitive_count - 1
self.primitive_count = primitive_count
self.bvh = BvhNode.field(shape=[self.primitive_count + self.internal_node_count])
self.codes = ti.field(dtype=ti.i32, shape=[self.primitive_count])
self.scene_ext = scene_ext
self.sorter = sorter
self.atomic_counter = ti.field(dtype=ti.i32, shape=[self.internal_node_count])
self.cook_bvh_gpu()
self.atomic_counter = None
@ti.kernel
def generate_morton_code(self, primitives: ti.template()):
for i in range(self.primitive_count):
bound = primitives[i].bound
centroid = (bound.max + bound.min) * 0.5
offset = centroid - self.scene_ext.min
total_ext = self.scene_ext.max - self.scene_ext.min
x = offset * 1024 / total_ext
self.codes[i] = left_3_shift(ti.floor(x[0], dtype=ti.i32)) | left_3_shift(ti.math.floor(x[1], dtype=ti.i32)) << 1 \
| left_3_shift(ti.math.floor(x[2], dtype=ti.i32)) << 2
@ti.func
def delta(self, n1: ti.i32, n2: ti.i32):
rv = -1
if 0 <= n2 < self.primitive_count:
c1 = self.codes[n1]
c2 = self.codes[n2]
v = 0
if c1 == c2:
c1 = n1
c2 = n2
v = 31
c = c1 ^ c2
rv = 31 - ti.math.floor(ti.math.log(c) / 0.69314, dtype=ti.i32) + v
return rv
@ti.kernel
def build_bvh(self):
# from https://research.nvidia.com/sites/default/files/publications/karras2012hpg_paper.pdf
for i in range(self.internal_node_count):
d = int(ti.math.sign(self.delta(i, i + 1) - self.delta(i, i - 1)))
delta_min = self.delta(i, i - d)
l_max = 2
while self.delta(i, i + l_max * d) > delta_min:
l_max = l_max * 2
l = 0
t = l_max // 2
while t >= 1:
if self.delta(i, i + (l + t) * d) > delta_min:
l = l + t
t = t // 2
j = i + l * d
delta_node = self.delta(i, j)
s = 0
t = l
div = 2
while t > 1:
# from floor to ceiling
t = ti.ceil(l / div, dtype=ti.i32)
if self.delta(i, i + (s + t) * d) > delta_node:
s = s + t
div *= 2
gama = i + s * d + ti.min(d, 0)
left = 0
right = 0
if ti.min(i, j) == gama:
left = gama + self.internal_node_count
else:
left = gama
if ti.max(i, j) == gama + 1:
right = gama + 1 + self.internal_node_count
else:
right = gama + 1
self.bvh[i].left = left
self.bvh[i].right = right
self.bvh[i].primitive = -1
self.bvh[left].parent = i
self.bvh[right].parent = i
@ti.kernel
def assign_bound(self):
"""
as the original paper says:
'Each thread starts from one leaf node and walks up the tree using parent pointers that we record during radix
tree construction. We track how many threads have visited each internal node using atomic counters—the first
thread terminates immediately while the second one gets to process the node. This way, each node is processed by
exactly one thread, which leads to O(n) time complexity'
"""
for i in range(self.primitive_count):
idx = i + self.internal_node_count
idx = self.bvh[idx].parent
while idx >= 0:
counter = ti.atomic_add(self.atomic_counter[idx], 1)
if counter == 0:
idx = -1
else:
left = self.bvh[idx].left
right = self.bvh[idx].right
bound = merge_bound(self.bvh[left].bound, self.bvh[right].bound)
self.bvh[idx].bound = bound
idx = self.bvh[idx].parent
@ti.kernel
def set_primitive_idx(self, primitives: ti.template()):
for i in range(self.primitive_count):
self.bvh[i + self.internal_node_count].primitive = i
self.bvh[i + self.internal_node_count].bound = primitives[i].bound
def sort(self):
output_primitive = Primitive.field(shape=[self.primitive_count])
if self.sorter == "radix_cpu":
RadixSorterCpu(self.primitives, output_primitive, self.primitive_count, self.codes)
elif self.sorter == "radix_gpu":
RadixSorterGpu(self.primitives, output_primitive, self.primitive_count, self.codes)
elif self.sorter == "bi_gpu":
BiSorterGpu(self.primitives, output_primitive, self.primitive_count, self.codes)
self.primitives = output_primitive
def cook_bvh_gpu(self):
self.generate_morton_code(self.primitives)
self.sort()
self.bvh[0].parent = -1
self.build_bvh()
self.set_primitive_idx(self.primitives)
self.assign_bound()
self.codes = None
bvh的构建主要参考了 Maximizing Parallelism in the Construction of BVHs, Octrees, and k-d Trees
双调排序参考了写了一个基于taichi的双调排序算法~
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非常赞，还没测试，想问下您自己测试的bvh树构建速度，以及基于bvh树做查询的速度怎样？有实验数据么？