Shape As Points: A Differentiable Poisson Solver
Authors: Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To investigate the aforementioned properties, we perform a set of controlled experiments. Moreover, we demonstrate state-of-the-art performance in reconstructing surface geometry from unoriented point clouds in two settings: an optimization-based setting that does not require training and is applicable to a wide range of shapes, and a learning-based setting for conditional shape reconstruction that is robust to noisy point clouds and outliers. |
| Researcher Affiliation | Collaboration | Songyou Peng1,2 Chiyu Max Jiang Yiyi Liao2,3 Michael Niemeyer2,3 Marc Pollefeys1,4 Andreas Geiger2,3 1ETH Zurich 2Max Planck Institute for Intelligent Systems, Tübingen 3University of Tübingen 4Microsoft |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/autonomousvision/shape_as_points. |
| Open Datasets | Yes | Datasets: We use the following datasets for optimization-based reconstruction: 1) Thingi10K [71], 2) Surface reconstruction benchmark (SRB) [65] and 3) D-FAUST [6]. Similar to prior works, we use 5 objects per dataset [19,23,65]. For learning-based object-level reconstruction, we consider all 13 classes of the Shape Net [9] subset, using the train/val/test split from [11]. |
| Dataset Splits | Yes | For learning-based object-level reconstruction, we consider all 13 classes of the Shape Net [9] subset, using the train/val/test split from [11]. |
| Hardware Specification | Yes | On average, our method requires 12 ms for computing a 1283 indicator grid from 15K points on a single NVIDIA GTX 1080Ti GPU. Computing a 2563 indicator grid requires 140 ms. Optimization time is evaluated on a single GTX 1080Ti GPU for IGR, Point2Mesh and our method. |
| Software Dependencies | No | The paper states 'We implement all models in Py Torch [51] and use the Adam optimizer [30]' and refers to 'scikit-image [60]', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We implement all models in Py Torch [51] and use the Adam optimizer [30] with a learning rate of 5e-4. We choose k = 7 for all learning-based reconstruction experiments. More specifically, we start optimizing at an indicator grid resolution of 323 for 1000 iterations, from which we obtain a coarse shape. Next, we sample from this coarse mesh and continue optimization at a resolution of 643 for 1000 iterations. We repeat this process until we reach the target resolution (2563) at which we acquire the final output mesh. |