Fast calculation of CDF / rolling join on multiple columns

I'm trying to measure the empirical cumulative distribution of some data in a multivariate setting. That is, given a dataset like

library(data.table)  # v 1.9.7

set.seed(2016)
dt <- data.table(x=rnorm(1000), y=rnorm(1000), z=rnorm(1000))
dt
             x        y       z
   1: -0.91474  2.07025 -1.7499
   2:  1.00125 -1.80941 -1.3856
   3: -0.05642  1.58499  0.8110
   4:  0.29665 -1.16660  0.3757
   5: -2.79147 -1.75526  1.2851
  ---                          
 996:  0.63423  0.13597 -2.3710
 997:  0.21415  1.03161 -1.5440
 998:  1.15357 -1.63713  0.4191
 999:  0.79205 -0.56119  0.6670
1000:  0.19502 -0.05297 -0.3288

I want to count the number of samples such that (x <= X, y <= Y, z <= Z) for some grid of (X, Y, Z) upper bounds like

bounds <- CJ(X=seq(-2, 2, by=.1), Y=seq(-2, 2, by=.1), Z=seq(-2, 2, by=.1))
bounds
        X  Y    Z
    1: -2 -2 -2.0
    2: -2 -2 -1.9
    3: -2 -2 -1.8
    4: -2 -2 -1.7
    5: -2 -2 -1.6
   ---           
68917:  2  2  1.6
68918:  2  2  1.7
68919:  2  2  1.8
68920:  2  2  1.9
68921:  2  2  2.0

Now, I've figured out that I can elegantly do this (using non-equi joins)

dt[, Count := 1]
result <- dt[bounds, on=c("x<=X", "y<=Y", "z<=Z"), allow.cartesian=TRUE][, list(N.cum = sum(!is.na(Count))), keyby=list(X=x, Y=y, Z=z)]
result[, CDF := N.cum/nrow(dt)]
result
        X  Y    Z N.cum   CDF
    1: -2 -2 -2.0     0 0.000
    2: -2 -2 -1.9     0 0.000
    3: -2 -2 -1.8     0 0.000
    4: -2 -2 -1.7     0 0.000
    5: -2 -2 -1.6     0 0.000
   ---                       
68917:  2  2  1.6   899 0.899
68918:  2  2  1.7   909 0.909
68919:  2  2  1.8   917 0.917
68920:  2  2  1.9   924 0.924
68921:  2  2  2.0   929 0.929

But this method is really inefficient and gets very slow as I start increasing the bin count. I think a multivariate version of data.table's rolling join functionality would do the trick, but that's not possible to my knowledge. Any suggestions to speed this up?