![](bg7.png)
Figure 12: Exemplary results of the view synthesis: (a) original camera view, (b) synthesis with source data, (c) voxel
resampling, (d) mesh resampling, (d) and (e) temporal filtering depth of k = 2 for voxel resampling and mesh resampling,
respectively.
resampling (d) without temporal filtering, the respec-
tive synthesis results with a temporal filtering depth
k = 2 in (e) and (f). While effects in the temporal
domain are hard to notice from single frames, synthe-
sis artifacts related to depth map artifacts on object
boundaries and revealed occlusions can be noticed
in (b) which do not occur in (c) to (f). Overall, the
quality of synthesis results with enhanced depth maps
does not vary significantly with a negligible positive
margin for the voxel based approach and temporal fil-
tering.
4 SUMMARY AND
CONCLUSIONS
This work presents algorithms to resample and filter
point cloud data reconstructed from multiple cameras
and multiple time instants. In an initial resampling
stage a voxel and a surface mesh based approach are
presented to resample the point cloud data into a com-
mon sampling grid. Subsequently, the resampled data
undergoes a filtering stage based on clustering to re-
move artifacts of depth estimation and achieve spa-
tiotemporal consistency. The presented algorithms
are evaluated in a view synthesis scenario. Results
show that view synthesis with enhanced depth maps
as produced by the algorithms leads to less artifacts
than synthesis with the original source data. The dif-
ference in achieved quality between the voxel and the
surface mesh based approach is negligible and with
regard to the computational complexity of the surface
mesh reconstruction, the voxel based approach is the
desirable solution for resampling.
Filtering in the temporal domain shows slight syn-
thesis quality improvements when moving objects are
confined to a limited region of the scene as in the bal-
let data set. For data sets in which moving objects
cover larger areas such as in the breakdancer data set,
temporal filtering does not improve synthesis results
compared to filtering across cameras. The presented
motion masking excludes samples within a relatively
wide image area with respect to the actual moving ob-
ject. Therefore, depth map artifacts in the correspond-
ing areas are not interpolated in the filtering stage and
thus affect synthesis.
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