SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks

By Alexandre Boulch, Joris Guerry, Bertrand Le Saux, Nicolas Audebert. Computers & graphics, 2017

366 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/900a4e66c541dd9734d4c44fdceb74a1740971e4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. S. Chappuis
2. Juergen Gall
3. Sven Behnke
4. Cyrill Stachniss
5. Gulcan Can
6. Andres Milioto
7. Alessandro Giusti
8. Jens Behley

In Environmental Engineering

1. Cyrill Stachniss
2. Jens Behley
3. Andres Milioto
4. Juergen Gall
5. Sven Behnke
6. Martin Garbade
7. Jan Quenzel

In Geology

1. Cyrill Stachniss
2. Jens Behley
3. Andres Milioto
4. Juergen Gall
5. Sven Behnke
6. Martin Garbade
7. Jan Quenzel

Citations in the corpus

• SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019, methodology): “To overcome the limitations of the voxel-based representation, such as the exploding memory consumption when the resolution of the voxel grid increases, more recent approaches either upsample voxel-predictions [53] using a CRF or use different representations, like more efficient spatial subdivision”
• Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset (2020, background): “…learning [11], [39], [4] and represent input data as voxels [40], points [24] or meshes [17]; other approaches render multiple views of the 3D scene and then rely on 2D semantic segmentation models [30], [31], [5], which can be trained on more abundant 2D labeled semantic segmentation datasets.”