Name
Abstract | ||
|---|---|---|
Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reconstruction approaches directly predict the entire point cloud in a single stage. Although this technique can handle low-resolution point clouds, it is not a viable solution for generating dense, high-resolution outputs. In this work, we introduce DensePCR, a deep pyramidal network for point cloud reconstruction that hierarchically predicts point clouds of increasing resolution. Towards this end, we propose an architecture that first predicts a low-resolution point cloud, and then hierarchically increases the resolution by aggregating local and global point features to deform a grid. Our method generates point clouds that are accurate, uniform and dense. Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a light-weight and scalable architecture for predicting high-resolution outputs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WACV.2019.00117 | 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) |
Keywords | DocType | Volume |
Three-dimensional displays,Image reconstruction,Image resolution,Shape,Surface reconstruction,Training,Computer architecture | Journal | abs/1901.08906 |
ISSN | ISBN | Citations |
2472-6737 | 978-1-7281-1975-5 | 6 |
PageRank | References | Authors |
0.54 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Priyanka Mandikal | 1 | 20 | 2.11 |
| R. Venkatesh Babu | 2 | 1046 | 84.83 |