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raht_torch.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on May 25, 2021
Modified on Jul 20, 2024
This code is derived by the implementation of 3DAC. See https://fatpeter.github.io/ for more details.
It is an python version of RAHT based on https://github.com/digitalivp/RAHT/tree/reorder.
The original C implementation is more readable.
"""
import numpy as np
import torch
# morton coding
# convert voxlized and deduplicated point cloud to morton code
def copyAsort(V):
# input
# V: np.array (n,3), input vertices
# output
# W: np.array (n,), weight
# val: np.array (n,), zyx val of vertices
# reord: np.array (n,), idx ord after sort
V=V.astype(np.uint64)
# w of leaf node sets to 1
W=np.ones(V.shape[0])
# encode zyx (pos) to bin
vx, vy, vz= V[:,2], V[:,1], V[:,0]
val = ((0x000001 & vx) ) + ((0x000001 & vy)<< 1) + ((0x000001 & vz)<< 2) + \
((0x000002 & vx)<< 2) + ((0x000002 & vy)<< 3) + ((0x000002 & vz)<< 4) + \
((0x000004 & vx)<< 4) + ((0x000004 & vy)<< 5) + ((0x000004 & vz)<< 6) + \
((0x000008 & vx)<< 6) + ((0x000008 & vy)<< 7) + ((0x000008 & vz)<< 8) + \
((0x000010 & vx)<< 8) + ((0x000010 & vy)<< 9) + ((0x000010 & vz)<<10) + \
((0x000020 & vx)<<10) + ((0x000020 & vy)<<11) + ((0x000020 & vz)<<12) + \
((0x000040 & vx)<<12) + ((0x000040 & vy)<<13) + ((0x000040 & vz)<<14) + \
((0x000080 & vx)<<14) + ((0x000080 & vy)<<15) + ((0x000080 & vz)<<16) + \
((0x000100 & vx)<<16) + ((0x000100 & vy)<<17) + ((0x000100 & vz)<<18) + \
((0x000200 & vx)<<18) + ((0x000200 & vy)<<19) + ((0x000200 & vz)<<20) + \
((0x000400 & vx)<<20) + ((0x000400 & vy)<<21) + ((0x000400 & vz)<<22) + \
((0x000800 & vx)<<22) + ((0x000800 & vy)<<23) + ((0x000800 & vz)<<24) + \
((0x001000 & vx)<<24) + ((0x001000 & vy)<<25) + ((0x001000 & vz)<<26) + \
((0x002000 & vx)<<26) + ((0x002000 & vy)<<27) + ((0x002000 & vz)<<28) + \
((0x004000 & vx)<<28) + ((0x004000 & vy)<<29) + ((0x004000 & vz)<<30) + \
((0x008000 & vx)<<30) + ((0x008000 & vy)<<31) + ((0x008000 & vz)<<32) + \
((0x010000 & vx)<<32) + ((0x010000 & vy)<<33) + ((0x010000 & vz)<<34) + \
((0x020000 & vx)<<34) + ((0x020000 & vy)<<35) + ((0x020000 & vz)<<36) + \
((0x040000 & vx)<<36) + ((0x040000 & vy)<<37) + ((0x040000 & vz)<<38) + \
((0x080000 & vx)<<38) + ((0x080000 & vy)<<39) + ((0x080000 & vz)<<40)
# + \
# ((0x100000 & vx)<<40) + ((0x100000 & vy)<<41) + ((0x100000 & vz)<<42) + \
# ((0x200000 & vx)<<42) + ((0x200000 & vy)<<43) + ((0x200000 & vz)<<44) + \
# ((0x400000 & vx)<<44) + ((0x400000 & vy)<<45) + ((0x400000 & vz)<<46) + \
# ((0x800000 & vx)<<46) + ((0x800000 & vy)<<47) + ((0x800000 & vz)<<48)
reord=np.argsort(val)
val=np.sort(val)
val = val.astype(np.uint64)
return W, val, reord
# morton decoding
# convert morton code to point cloud
def val2V(val, factor):
'''
Parameters
----------
val : morton code
factor : shift morton code for deocoding
Returns
-------
V_re : point cloud
'''
if factor>2 or factor<0:
print('error')
return
val = val<<factor
V_re = np.zeros((val.shape[0],3))
V_re[:,2] = (0x000001 & val) + \
(0x000002 & (val>> 2)) + \
(0x000004 & (val>> 4)) + \
(0x000008 & (val>> 6)) + \
(0x000010 & (val>> 8)) + \
(0x000020 & (val>>10)) + \
(0x000040 & (val>>12)) + \
(0x000080 & (val>>14)) + \
(0x000100 & (val>>16)) + \
(0x000200 & (val>>18)) + \
(0x000400 & (val>>20)) + \
(0x000800 & (val>>22)) + \
(0x001000 & (val>>24)) + \
(0x002000 & (val>>26)) + \
(0x004000 & (val>>28)) + \
(0x008000 & (val>>30)) + \
(0x010000 & (val>>32)) + \
(0x020000 & (val>>34)) + \
(0x040000 & (val>>36)) + \
(0x080000 & (val>>38)) + \
(0x100000 & (val>>40))
# + \
# (0x200000 & (val>>42)) + \
# (0x400000 & (val>>44)) + \
# (0x800000 & (val>>46))
V_re[:,1] = (0x000001 & (val>> 1)) + \
(0x000002 & (val>> 3)) + \
(0x000004 & (val>> 5)) + \
(0x000008 & (val>> 7)) + \
(0x000010 & (val>> 9)) + \
(0x000020 & (val>>11)) + \
(0x000040 & (val>>13)) + \
(0x000080 & (val>>15)) + \
(0x000100 & (val>>17)) + \
(0x000200 & (val>>19)) + \
(0x000400 & (val>>21)) + \
(0x000800 & (val>>23)) + \
(0x001000 & (val>>25)) + \
(0x002000 & (val>>27)) + \
(0x004000 & (val>>29)) + \
(0x008000 & (val>>31)) + \
(0x010000 & (val>>33)) + \
(0x020000 & (val>>35)) + \
(0x040000 & (val>>37)) + \
(0x080000 & (val>>39)) + \
(0x100000 & (val>>41))
# + \
# (0x200000 & (val>>43)) + \
# (0x400000 & (val>>45)) + \
# (0x800000 & (val>>47))
V_re[:,0] = (0x000001 & (val>> 2)) + \
(0x000002 & (val>> 4)) + \
(0x000004 & (val>> 6)) + \
(0x000008 & (val>> 8)) + \
(0x000010 & (val>>10)) + \
(0x000020 & (val>>12)) + \
(0x000040 & (val>>14)) + \
(0x000080 & (val>>16)) + \
(0x000100 & (val>>18)) + \
(0x000200 & (val>>20)) + \
(0x000400 & (val>>22)) + \
(0x000800 & (val>>24)) + \
(0x001000 & (val>>26)) + \
(0x002000 & (val>>28)) + \
(0x004000 & (val>>30)) + \
(0x008000 & (val>>32)) + \
(0x010000 & (val>>34)) + \
(0x020000 & (val>>36)) + \
(0x040000 & (val>>38)) + \
(0x080000 & (val>>40)) + \
(0x100000 & (val>>42))
# + \
# (0x200000 & (val>>44)) + \
# (0x400000 & (val>>46)) + \
# (0x800000 & (val>>48))
if factor == 1:
V_re[:,2]/=2
if factor == 2:
V_re[:,1]/=2
V_re[:,2]/=2
return V_re
def transform_batched(a0, a1, C0, C1):
# input
# a0, a1: float, weight
# C0, C1: np.array (n,), att of vertices
# output
# v0, v1: np.array (n,), trans att of vertices
trans_matrix=np.array([[a0, a1],
[-a1, a0]])
trans_matrix=trans_matrix.transpose((2,0,1))
V=np.matmul(trans_matrix, np.concatenate((C0,C1),1))
return V[:,0], V[:,1]
def transform_batched_torch(a0, a1, C0, C1):
# print(a0.shape)
t0 = torch.tensor(a0[:,None]).cuda().float()
t1 = torch.tensor(a1[:,None]).cuda().float()
V0 = t0*C0+t1*C1
V1 = -t1*C0+t0*C1
# temp1 = a0[:,None]
# temp2 = a1[:,None]
# trans_matrix = np.concatenate((temp1, temp2, -temp2, temp1),1)
# trans_matrix = trans_matrix.reshape(-1,2,2)
# trans_matrix = torch.tensor(trans_matrix).to(C0.get_device()).float()
# print('trans_matrix.shape', trans_matrix.shape)
# print('trans_matrix.shape', C0.shape)
# print('torch.cat((C0,C1),1).shape', torch.cat((C0,C1),1).shape)
# V=torch.matmul(trans_matrix, torch.cat((C0,C1),1))
return V0, V1
def itransform_batched(a0, a1, CT0, CT1):
# input
# a0, a1: float, weight
# CT0, CT1: np.array (n,), trans att of vertices
# output
# c0, c1: np.array (n,), att of vertices
trans_matrix=np.array([[a0, -a1],
[a1, a0]])
trans_matrix=trans_matrix.transpose((2,0,1))
C=np.matmul(trans_matrix, np.concatenate((CT0,CT1),1))
return C[:,0], C[:,1]
def itransform_batched_torch(a0, a1, CT0, CT1):
# input
# a0, a1: float, weight
# CT0, CT1: np.array (n,), trans att of vertices
# output
# c0, c1: np.array (n,), att of vertices
# trans_matrix=np.array([[a0, -a1],
# [a1, a0]])
# trans_matrix=trans_matrix.transpose((2,0,1))
# C=np.matmul(trans_matrix, np.concatenate((CT0,CT1),1))
# return C[:,0], C[:,1]
t0 = torch.tensor(a0[:,None]).cuda().float()
t1 = torch.tensor(a1[:,None]).cuda().float()
V0 = t0*CT0-t1*CT1
V1 = t1*CT0+t0*CT1
return V0, V1
def haar3D(inV, inC, depth):
'''
Parameters
----------
inV : point cloud geometry(pre-voxlized and deduplicated)
inC : attributes
depth : depth level of geometry(octree)
Returns
-------
res : transformed coefficients and side information
'''
import copy
inC = copy.deepcopy(inC)
# N,NN number of points
# K, dims (3) of geometry
N, K = inC.shape
NN = N
# depth of RAHT tree (without leaf node level)
depth *= 3
# print('depth', depth)
# low_freq coeffs for transmitting coeffs (high_freq)
# low_freq = np.zeros(inC.shape)
wT = np.zeros((N, )).astype(np.uint64)
valT = np.zeros((N, )).astype(np.uint64)
posT = np.zeros((N, )).astype(np.int64)
# position of coeffs
node_xyz = np.zeros((N, 3))-1
depth_CT = np.zeros((N, ))-1
# morton coding
# return weight, morton code, map from inV to val
w, val, TMP = copyAsort(inV)
# pos, order from transformed coeffes to morton sorted attributes
pos = np.arange(N)
C = inC[TMP].astype(np.float64)
# low_freq for each depth
iCT_low=[]
# parent idx for each depth
iparent=[]
# weight for each depth
iW=[]
# node position for each depth
iPos=[]
for d in range(depth):
# print('-'*10, 'd:', d, '-'*10)
# num of nodes for current depth
S = N
# 1D example (trans val 1 and 4, merge 2 and 3)
# 01234567
# idx: 0, 1, 2, 3
# val: 1, 2, 3, 4
# merge two leaf nodes or not
# mask: False, True, False, False
# combine two neighbors or transmit
# combine idx: 1
# trans idx: 0, 3
# merge two leaf nodes or not
temp=val.astype(np.uint64)&0xFFFFFFFFFFFFFFFE
mask=temp[:-1]==temp[1:]
mask=np.concatenate((mask,[False]))
# 2 types of idx for current level of RAHT tree
# combine two neighbors or transmit
comb_idx_array=np.where(mask==True)[0]
trans_idx_array=np.where(mask==False)[0]
trans_idx_array=np.setdiff1d(trans_idx_array, comb_idx_array+1)
# print('comb_idx_array.shape', comb_idx_array.shape)
# print('trans_idx_array.shape', trans_idx_array.shape)
# 2 types of idx for next level of RAHT tree
# idxT_array, idx of low-freq for next depth level
# maskT == False for trans (not merge two leaf nodes)
# maskT == True for comb (merge two leaf nodes)
# maskT: False, True, False (1D example)
idxT_array=np.setdiff1d(np.arange(S), comb_idx_array+1)
maskT=mask[idxT_array]
# 2 types of weight for next level of RAHT tree
# wT[N] = wT[M] (not merge two leaf nodes)
# wT[M] = w[i] + w[j] (merge two leaf nodes)
# print(w.shape)
# print(wT.shape)
# print(wT[np.where(maskT==True)[0]].shape)
# print((w[comb_idx_array]+w[comb_idx_array+1]).shape)
wT[np.where(maskT==False)[0]] = w[trans_idx_array]
wT[np.where(maskT==True)[0]] = w[comb_idx_array]+w[comb_idx_array+1]
# pos is used to connect C and val/w (current level)
# posT is used to connect C and val/w (next level)
# pos: 0, 1, 2, 3
# posT: 0, 1, 3, *
posT[np.where(maskT==False)[0]] = pos[trans_idx_array]
posT[np.where(maskT==True)[0]] = pos[comb_idx_array]
# transform attr to coeff
left_node_array, right_node_array = comb_idx_array, comb_idx_array+1
a = np.sqrt((w[left_node_array])+(w[right_node_array]))
C[pos[left_node_array]], C[pos[right_node_array]] = transform_batched(np.sqrt((w[left_node_array]))/a,
np.sqrt((w[right_node_array]))/a,
C[pos[left_node_array],None],
C[pos[right_node_array],None])
# collect side information for current depth
parent=np.arange(S)
parent_t=np.zeros(S)
parent_t[right_node_array]=1
parent_t = parent_t.cumsum()
parent = parent-parent_t
# collected but not used in paper
iparent.append(parent.astype(int))
# High-freq nodes do not exist in the leaf level, thus collect information from the next depth.
# collect side information after transform for next depth
iCT_low.append(C[pos[idxT_array]])
num_nodes = N-comb_idx_array.shape[0]
iW.append(wT[:num_nodes]+0)
Pos_t = val2V(val, d%3)[idxT_array]
if d%3 == 0:
Pos_t[:,2]=Pos_t[:,2]//2
if d%3 == 1:
Pos_t[:,1]=Pos_t[:,1]//2
if d%3 == 2:
Pos_t[:,0]=Pos_t[:,0]//2
iPos.append(Pos_t)
# collect side information of high_freq nodes for next depth
# tree node feature extraction without considering low-freq nodes
# low_freq[pos[right_node_array]]=C[pos[left_node_array]]
node_xyz[pos[right_node_array]] = val2V(val[right_node_array], d%3)
if d%3 == 0:
node_xyz[pos[right_node_array],2]=node_xyz[pos[right_node_array],2]//2
if d%3 == 1:
node_xyz[pos[right_node_array],1]=node_xyz[pos[right_node_array],1]//2
if d%3 == 2:
node_xyz[pos[right_node_array],0]=node_xyz[pos[right_node_array],0]//2
depth_CT[pos[trans_idx_array]] = d
depth_CT[pos[left_node_array]], depth_CT[pos[right_node_array]] = d, d
# end of information collection
# valT, morton code for the next depth
valT = (val >> 1)[idxT_array]
# num of leaf nodes for next level
N_T=N
N=N-comb_idx_array.shape[0]
# move pos,w of high-freq nodes in the end
# pos: 0, 1, 2, 3
# posT: 0, 1, 3, *
# posT: 0, 1, 3, 2
# transpose
N_idx_array=np.arange(N_T, N, -1)-NN-1
wT[N_idx_array]=wT[np.where(maskT==True)[0]]
posT[N_idx_array]=pos[comb_idx_array+1]
# move transposed pos,w of high-freq nodes in the end
pos[N:S] = posT[N:S]
w[N:S] = wT[N:S]
val, valT = valT, val
pos, posT = posT, pos
w, wT = wT, w
outW=np.zeros(w.shape)
outW[pos]=w
# print('iCT_low[-1].shape', iCT_low[-1].shape)
# print('low_freq.shape', low_freq.shape)
# low_freq[0] = iCT_low[-1]
res = {'CT':C,
'w':outW,
'depth_CT':depth_CT,
'node_xyz':node_xyz,
# 'low_freq':low_freq,
'iCT_low':iCT_low,
'iW':iW,
'iPos':iPos,
'iparent':iparent,
}
return res
def haar3D_torch(inC, depth, w, val, TMP):
'''
Parameters
----------
inV : point cloud geometry(pre-voxlized and deduplicated)
inC : attributes
depth : depth level of geometry(octree)
Returns
-------
res : transformed coefficients and side information
'''
N, K = inC.shape
NN = N
# depth of RAHT tree (without leaf node level)
depth *= 3
# print('depth', depth)
wT = np.zeros((N, )).astype(np.uint64)
valT = np.zeros((N, )).astype(np.uint64)
posT = np.zeros((N, )).astype(np.int64)
# position of coeffs
node_xyz = np.zeros((N, 3))-1
# depth_CT = np.zeros((N, ))-1
# morton coding
# return weight, morton code, map from inV to val
# w, val, TMP = copyAsort(inV)
pos = np.arange(N)
C = inC[torch.tensor(TMP)]
# .astype(torch.float64)
# parent idx for each depth
# iparent=[]
# weight for each depth
# iW=[]
# node position for each depth
# iPos=[]
for d in range(depth):
S = N
# merge two leaf nodes or not
temp=val.astype(np.uint64)&0xFFFFFFFFFFFFFFFE
mask=temp[:-1]==temp[1:]
mask=np.concatenate((mask,[False]))
# 2 types of idx for current level of RAHT tree
# combine two neighbors or transmit
comb_idx_array=np.where(mask==True)[0]
trans_idx_array=np.where(mask==False)[0]
trans_idx_array=np.setdiff1d(trans_idx_array, comb_idx_array+1)
# print('comb_idx_array.shape', comb_idx_array.shape)
# print('trans_idx_array.shape', trans_idx_array.shape)
# 2 types of idx for next level of RAHT tree
# idxT_array, idx of low-freq for next depth level
# maskT == False for trans (not merge two leaf nodes)
# maskT == True for comb (merge two leaf nodes)
# maskT: False, True, False (1D example)
idxT_array=np.setdiff1d(np.arange(S), comb_idx_array+1)
maskT=mask[idxT_array]
# 2 types of weight for next level of RAHT tree
# wT[N] = wT[M] (not merge two leaf nodes)
# wT[M] = w[i] + w[j] (merge two leaf nodes)
wT[np.where(maskT==False)[0]] = w[trans_idx_array]
wT[np.where(maskT==True)[0]] = w[comb_idx_array]+w[comb_idx_array+1]
# pos is used to connect C and val/w (current level)
# posT is used to connect C and val/w (next level)
# pos: 0, 1, 2, 3
# posT: 0, 1, 3, *
posT[np.where(maskT==False)[0]] = pos[trans_idx_array]
posT[np.where(maskT==True)[0]] = pos[comb_idx_array]
# transform attr to coeff
left_node_array, right_node_array = comb_idx_array, comb_idx_array+1
a = np.sqrt((w[left_node_array])+(w[right_node_array]))
C[pos[left_node_array]], C[pos[right_node_array]] = transform_batched_torch(np.sqrt((w[left_node_array]))/a,
np.sqrt((w[right_node_array]))/a,
C[pos[left_node_array],None],
C[pos[right_node_array],None])
# collect side information for current depth
parent=np.arange(S)
parent_t=np.zeros(S)
parent_t[right_node_array]=1
parent_t = parent_t.cumsum()
parent = parent-parent_t
# collected but not used in paper
# iparent.append(parent.astype(int))
# High-freq nodes do not exist in the leaf level, thus collect information from the next depth.
# collect side information after transform for next depth
# iCT_low.append(C[pos[idxT_array]].cpu().numpy())
# num_nodes = N-comb_idx_array.shape[0]
# iW.append(wT[:num_nodes]+0)
Pos_t = val2V(val, d%3)[idxT_array]
if d%3 == 0:
Pos_t[:,2]=Pos_t[:,2]//2
if d%3 == 1:
Pos_t[:,1]=Pos_t[:,1]//2
if d%3 == 2:
Pos_t[:,0]=Pos_t[:,0]//2
# iPos.append(Pos_t)
# collect side information of high_freq nodes for next depth
# tree node feature extraction without considering low-freq nodes
# low_freq[pos[right_node_array]]=C[pos[left_node_array]].cpu().numpy()
node_xyz[pos[right_node_array]] = val2V(val[right_node_array], d%3)
if d%3 == 0:
node_xyz[pos[right_node_array],2]=node_xyz[pos[right_node_array],2]//2
if d%3 == 1:
node_xyz[pos[right_node_array],1]=node_xyz[pos[right_node_array],1]//2
if d%3 == 2:
node_xyz[pos[right_node_array],0]=node_xyz[pos[right_node_array],0]//2
# depth_CT[pos[trans_idx_array]] = d
# depth_CT[pos[left_node_array]], depth_CT[pos[right_node_array]] = d, d
# end of information collection
# valT, morton code for the next depth
valT = (val >> 1)[idxT_array]
# num of leaf nodes for next level
N_T=N
N=N-comb_idx_array.shape[0]
# move pos,w of high-freq nodes in the end
# pos: 0, 1, 2, 3
# posT: 0, 1, 3, *
# posT: 0, 1, 3, 2
# transpose
N_idx_array=np.arange(N_T, N, -1)-NN-1
wT[N_idx_array]=wT[np.where(maskT==True)[0]]
posT[N_idx_array]=pos[comb_idx_array+1]
# move transposed pos,w of high-freq nodes in the end
pos[N:S] = posT[N:S]
w[N:S] = wT[N:S]
val, valT = valT, val
pos, posT = posT, pos
w, wT = wT, w
return C
def get_RAHT_tree(inV, depth):
'''
Parameters
----------
inV : point cloud geometry(pre-voxlized and deduplicated)
depth : depth level of geometry(octree)
Returns
-------
res : tree without low- and high-freq coeffs
'''
# N,NN number of points
# K, dims (3) of geometry
N, _ = inV.shape
NN = N
depth *= 3
wT = np.zeros((N, ))
valT = np.zeros((N, )).astype(np.uint64)
posT = np.zeros((N, )).astype(np.uint64)
# morton code and weight for each depth level
iVAL = np.zeros((depth, N)).astype(np.uint64)
iW = np.zeros((depth, N))
# M, num of nodes for current depth level
M = N
# num of nodes for each depth level
iM = np.zeros((depth, )).astype(np.uint64)
w, val, reord = copyAsort(inV)
pos = np.arange(N).astype(np.uint64)
# construct RAHT tree from bottom to top, similar to RAHT encoding
# obtain iVAL, iW, iM for RAHT decoding
for d in range(depth):
iVAL[d,:M] = val[:M]
iW[d,:M] = w[:M]
iM[d]= M
M = 0
S = N
temp=val.astype(np.uint64)&0xFFFFFFFFFFFFFFFE
mask=temp[:-1]==temp[1:]
mask=np.concatenate((mask,[False]))
comb_idx_array=np.where(mask==True)[0]
trans_idx_array=np.where(mask==False)[0]
trans_idx_array=np.setdiff1d(trans_idx_array, comb_idx_array+1)
idxT_array=np.setdiff1d(np.arange(S), comb_idx_array+1)
maskT=mask[idxT_array]
wT[np.where(maskT==False)[0]] = w[trans_idx_array]
posT[np.where(maskT==False)[0]] = pos[trans_idx_array]
wT[np.where(maskT==True)[0]] = w[comb_idx_array]+w[comb_idx_array+1]
posT[np.where(maskT==True)[0]] = pos[comb_idx_array]
valT = (val >> 1)[idxT_array]
N_T=N
N=N-comb_idx_array.shape[0]
M=N
N_idx_array=np.arange(N_T, N, -1)-NN-1
wT[N_idx_array]=wT[np.where(maskT==True)[0]]
posT[N_idx_array]=pos[comb_idx_array+1]
pos[N:S] = posT[N:S]
w[N:S] = wT[N:S]
val, valT = valT, val
pos, posT = posT, pos
w, wT = wT, w
# input attributes, morton sorted attributes, coeffs
# inC, C, CT
# inC and C are connected by reorder
# C and CT are connected by pos
res = {'reord':reord,
'pos':pos,
'iVAL':iVAL,
'iW':iW,
'iM':iM,
}
return res
def inv_haar3D(inV, inCT, depth):
'''
Parameters
----------
inV : point cloud geometry(pre-voxlized and deduplicated)
inCT : transformed coeffs (high-freq coeffs)
depth : depth level of geometry(octree)
Returns
-------
res : rec attributes
'''
# N,NN number of points
# K, dims (3) of geometry
N, K = inCT.shape
NN = N
depth *= 3
CT = np.zeros((N, K))
C = np.zeros((N, K))
outC = np.zeros((N, K))
res_tree = get_RAHT_tree(inV, depth)
reord, pos, iVAL, iW, iM = \
res_tree['reord'], res_tree['pos'], res_tree['iVAL'], res_tree['iW'], res_tree['iM']
CT = inCT[pos]
C = np.zeros(CT.shape)
# RAHT decoding from top to bottom
d = depth
while d:
d = d-1
S = iM[d]
M = iM[d-1] if d else NN
val, w = iVAL[d, :int(S)], iW[d, :int(S)]
M = 0
N = S
# get idx, similar to encoding
temp=val.astype(np.uint64)&0xFFFFFFFFFFFFFFFE
mask=temp[:-1]==temp[1:]
mask=np.concatenate((mask,[False]))
comb_idx_array=np.where(mask==True)[0]
trans_idx_array=np.where(mask==False)[0]
trans_idx_array=np.setdiff1d(trans_idx_array, comb_idx_array+1)
idxT_array=np.setdiff1d(np.arange(S), comb_idx_array+1)
maskT=mask[idxT_array.astype(int)]
# transmit low-freq
C[trans_idx_array] = CT[np.where(maskT==False)[0]]
# decode low_freq and high_freq to two low_freq coeffs
# N_idx_array, idx of high_freq
N_T=N
N=N-comb_idx_array.shape[0]
N_idx_array=np.arange(N_T, N, -1)-NN-1
left_node_array, right_node_array = comb_idx_array, comb_idx_array+1
a = np.sqrt((w[left_node_array])+(w[right_node_array]))
C[left_node_array], C[right_node_array] = itransform_batched(np.sqrt((w[left_node_array]))/a,
np.sqrt((w[right_node_array]))/a,
CT[np.where(maskT==True)[0]][:,None],
CT[N_idx_array.astype(int)][:,None])
CT[:S] = C[:S]
outC[reord] = C
return outC
def inv_haar3D_torch(inCT, depth, res_tree):
'''
Parameters
----------
inV : point cloud geometry(pre-voxlized and deduplicated)
inCT : transformed coeffs (high-freq coeffs)
depth : depth level of geometry(octree)
Returns
-------
res : rec attributes
'''
# N,NN number of points
# K, dims (3) of geometry
N, K = inCT.shape
NN = N
depth *= 3
# CT = torch.zeros((N, K), device='cuda')
# C = torch.zeros((N, K), device='cuda')
outC = torch.zeros((N, K), device='cuda')
reord, pos, iVAL, iW, iM = \
res_tree['reord'], res_tree['pos'], res_tree['iVAL'], res_tree['iW'], res_tree['iM']
# print('pos.shape', pos.shape)
# print('pos.type', type(pos[0]))
CT = inCT[torch.tensor(pos.astype(np.int64))]
C = torch.zeros(CT.shape, device='cuda')
# print('CT.shape, C.shape', CT.shape, C.shape)