IEEE Geoscience and Remote Sensing Magazine - December 2020 - 49
both unordered and unstructured problems of the raw point
cloud. Voxelized data can be further processed by 3D convolutions, as in the case of pixels in 2D neural networks.
Voxel-based architectures still have serious shortcomings.
The voxel structure has a low resolution compared to the point
cloud. Obviously, there is a loss in data representation. In addition, voxel structures not only store occupied spaces, but also
store free or unknown spaces, which can result in high computational and memory requirements.
The most well-known voxel-based 3D CNN is VoxNet
[187], but this has been tested only for object detection. On
the PCSS task, some papers, like [69], [98], [188], and [189],
proposed representative frameworks. SegCloud [98] is an endto-end PCSS framework that combines 3D full CNN, trilinear
interpolation, and fully connected conditional random fields
to complete the PCSS task. Figure 6 shows the framework of
SegCloud and illustrates the basic pipeline for voxel-based semantic segmentation. In SegCloud, raw point clouds are voxelized in the preprocessing step. Then a 3D fully CNN (FCNN)
is applied to generate downsampled voxel labels. After that,
a trilinear interpolation layer is employed to transfer voxel
labels back to 3D point labels. Finally, a 3D fully connected
CRF (FC-CRF) method is used to regularize previous 3D PCSS
results and acquire final results. SegCloud used to be the stateof-the-art approach in both S3DIS and Semantic3D.net, but
it provided no steps for addressing high computational and
memory problems related to fixed-sized voxels. With more advanced methods springing up, SegCloud has fallen from favor
in recent years.
To reduce unnecessary computation and memory consumption, the flexible octree structure is an effective replacement for fixed-size voxels in 3D CNNs. OctNet [69] and
Step 1: Pointwise Probabilistic Classification
3D Point
Cloud
Neighborhood
Selection
Feature
Extraction
Supervised
Classification
Initial
Labeling
Initial
Labeling
Adjacency Graph
Construction
Setup for
Regularization
Graph-Structured
Optimization
Smooth
Labeling
- Search Space
- Fidelity Term
- Penalizer
- Combinatorial
- Conv. Cont.
- Nonconv. Cont.
Step 2: Structured Regularization
FIGURE 4. The PCSS framework by [97]. The term semantic segmentation in our review is defined as " supervised classification " in [97].
Conv. Cont.: convex continuous. (Source: [97]; reprinted with permission from Elsevier.)
Image
Pairs
RGB Mesh
Semantized
Images
Semantized
Point Cloud
Input
Composite
Mesh
Mesh View
Generation
Semantic
Labeling
Back Projection
and Accumulation
FIGURE 5. The workflow of SnapNet. (Source: [67]; reprinted with permission from Elsevier.)
DECEMBER 2020
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
49
http://www.Semantic3D.net
IEEE Geoscience and Remote Sensing Magazine - December 2020
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