Learning Topology from Synthetic Data for Unsupervised Depth Completion

• URL: http://arxiv.org/abs/2106.02994v1
• Date: Sun, 6 Jun 2021 00:21:12 GMT
• Title: Learning Topology from Synthetic Data for Unsupervised Depth Completion
• Authors: Alex Wong, Safa Cicek, and Stefano Soatto
• Abstract summary: We present a method for inferring dense depth maps from images and sparse depth measurements. We learn the association of sparse point clouds with dense natural shapes, and using the image as evidence to validate the predicted depth map.
• Score: 66.26787962258346
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a method for inferring dense depth maps from images and sparse depth measurements by leveraging synthetic data to learn the association of sparse point clouds with dense natural shapes, and using the image as evidence to validate the predicted depth map. Our learned prior for natural shapes uses only sparse depth as input, not images, so the method is not affected by the covariate shift when attempting to transfer learned models from synthetic data to real ones. This allows us to use abundant synthetic data with ground truth to learn the most difficult component of the reconstruction process, which is topology estimation, and use the image to refine the prediction based on photometric evidence. Our approach uses fewer parameters than previous methods, yet, achieves the state of the art on both indoor and outdoor benchmark datasets. Code available at: https://github.com/alexklwong/learning-topology-synthetic-data.

Related papers

• Generalizing Dataset Distillation via Deep Generative Prior [75.9031209877651]
  We propose to distill an entire dataset's knowledge into a few synthetic images. The idea is to synthesize a small number of synthetic data points that, when given to a learning algorithm as training data, result in a model approximating one trained on the original data. We present a new optimization algorithm that distills a large number of images into a few intermediate feature vectors in the generative model's latent space.
  arXiv  Detail & Related papers  (2023-05-02T17:59:31Z)
• Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation [151.70234052015948]
  We propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7%.
  arXiv  Detail & Related papers  (2022-11-20T15:49:11Z)
• Towards Unpaired Depth Enhancement and Super-Resolution in the Wild [121.96527719530305]
  State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. We consider an approach to depth map enhancement based on learning from unpaired data.
  arXiv  Detail & Related papers  (2021-05-25T16:19:16Z)
• Learning non-rigid surface reconstruction from spatio-temporal image patches [0.0]
  We present a method to reconstruct a dense-temporal depth map of a deformable object from a video sequence. The estimation of depth is performed locally on non-temporal patches of the video, and the full depth video of entire shape is recovered by combining them together. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using other approaches like conventional non-rigid structure.
  arXiv  Detail & Related papers  (2020-06-18T20:25:15Z)
• Dataset Condensation with Gradient Matching [36.14340188365505]
  We propose a training set synthesis technique for data-efficient learning, called dataset Condensation, that learns to condense large dataset into a small set of informative synthetic samples for training deep neural networks from scratch. We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-10T16:30:52Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)
• DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data [110.29043712400912]
  We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation. Experiments show that our method outperforms previous methods on 8 datasets by a large margin with the zero-shot test setting.
  arXiv  Detail & Related papers  (2020-02-03T05:38:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.