From Point Clouds to Mesh Using Regression

By Lubor Ladicky, Olivier Saurer, Sohyeon Jeong, Fabio Maninchedda, Marc Pollefeys. 2017

56 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, 3D Vision & Pose

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. Andreas Geiger
2. Michael Niemeyer
3. Gerard Pons-Moll
4. Julian Chibane
5. Lars M. Mescheder
6. Michael Oechsle
7. Yiyi Liao
8. Songyou Peng

In Computer Graphics and Computer-Aided Design

1. Andreas Geiger
2. Michael Niemeyer
3. Gerard Pons-Moll
4. Yiyi Liao
5. Julian Chibane
6. Michael Oechsle
7. Lars M. Mescheder
8. Jonathan T. Barron

In Computer Vision and Pattern Recognition

1. Andreas Geiger
2. Luc Van Gool
3. Jitendra Malik
4. Andrea Vedaldi
5. Ming-Hsuan Yang
6. Dacheng Tao
7. Xiaogang Wang
8. Trevor Darrell

In 3D Vision & Pose

1. Andreas Geiger
2. Michael Niemeyer
3. Gerard Pons-Moll
4. Lars M. Mescheder
5. Michael Oechsle
6. Julian Chibane
7. Yiyi Liao
8. Aymen Mir

Citations in the corpus

• Occupancy Networks: Learning 3D Reconstruction in Function Space (2018, background): “For achieving sub-voxel precision, several works [12,42,60] have proposed to predict truncated signed distance fields (TSDF) [11] where each point in a 3D grid stores the truncated signed distance to the closest 3D surface point.”
• Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion (2020, background): “To smooth out noise, it is possible to represent shapes as Truncated Signed Distance functions [12] for learning [14, 36, 58, 64].”
• Neural Unsigned Distance Fields for Implicit Function Learning (2020, methodology): “Replacing occupancy with Truncated Signed Distance functions [17] for learning [19, 42, 65, 70] can reduce quantization artifacts, nonetheless TSDF values need to be stored in a grid of fixed limited resolution.”
• Deep Marching Cubes: Learning Explicit Surface Representations (2018, background): “Sub voxel precision can be achieved by exploiting the truncated signed distance function (TSDF) [8] as representation where each voxel stores the truncated signed distance to the closest 3D surface point [10, 28, 35].”
• Deep Stereo Using Adaptive Thin Volume Representation With Uncertainty Awareness (2019, background): “Recent work also tried to directly reconstruct surface meshes [34, 25, 53, 19, 46, 28], deformable shapes [24, 25], and some learned implicit distance functions [7, 41, 37, 6].”
• Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation (2019, unknown): “[25].”
• Learning Depth with Convolutional Spatial Propagation Network (2018, background): “Some other works directly convert 3D points from LiDAR to dense ones without image input [72], [73], [74], In”
• Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network (2018, background): “Some other works directly convert sparse 3D points to dense ones without image input [43,44,45], whereas the density of sparse points must be high enough to reveal the scene structure, which is not available in our scenario.”