Bayesian Deep Basis Fitting for Depth Completion with Uncertainty

• URL: http://arxiv.org/abs/2103.15254v1
• Date: Mon, 29 Mar 2021 00:40:02 GMT
• Title: Bayesian Deep Basis Fitting for Depth Completion with Uncertainty
• Authors: Chao Qu, Wenxin Liu, Camillo J. Taylor
• Abstract summary: We extend Deep Basis Fitting (DBF) for depth completion within a Bayesian evidence framework. By adopting a Bayesian treatment, our approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements.
• Score: 13.2830249140166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work we investigate the problem of uncertainty estimation for image-guided depth completion. We extend Deep Basis Fitting (DBF) for depth completion within a Bayesian evidence framework to provide calibrated per-pixel variance. The DBF approach frames the depth completion problem in terms of a network that produces a set of low-dimensional depth bases and a differentiable least squares fitting module that computes the basis weights using the sparse depths. By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements. We conduct controlled experiments to compare BDBF against commonly used techniques for uncertainty estimation under various scenarios. Results show that our method produces better uncertainty estimates with accurate depth prediction.

Related papers

• Revisiting Gradient-based Uncertainty for Monocular Depth Estimation [10.502852645001882]
  We introduce gradient-based uncertainty estimation for monocular depth estimation models. We demonstrate that our approach is effective in determining the uncertainty without re-training. In particular, for models trained with monocular sequences and therefore most prone to uncertainty, our method outperforms related approaches.
  arXiv  Detail & Related papers  (2025-02-09T17:21:41Z)
• Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion [51.69876947593144]
  Existing methods for depth completion operate in tightly constrained settings. Inspired by advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation. Marigold-DC builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance.
  arXiv  Detail & Related papers  (2024-12-18T00:06:41Z)
• ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation [62.600382533322325]
  We propose a novel monocular depth estimation method called ScaleDepth. Our method decomposes metric depth into scene scale and relative depth, and predicts them through a semantic-aware scale prediction module. Our method achieves metric depth estimation for both indoor and outdoor scenes in a unified framework.
  arXiv  Detail & Related papers  (2024-07-11T05:11:56Z)
• Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios [103.72094710263656]
  This paper presents a novel approach that identifies and integrates dominant cross-modality depth features with a learning-based framework. We propose a novel confidence loss steering a confidence predictor network to yield a confidence map specifying latent potential depth areas. With the resulting confidence map, we propose a multi-modal fusion network that fuses the final depth in an end-to-end manner.
  arXiv  Detail & Related papers  (2024-02-19T04:39:16Z)
• A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles [11.725077632618879]
  M4Depth is a state-of-the-art depth estimation method designed for unmanned aerial vehicle (UAV) applications. We show how M4Depth can be enhanced to perform joint depth and uncertainty estimation.
  arXiv  Detail & Related papers  (2023-05-31T12:13:45Z)
• Robust Depth Completion with Uncertainty-Driven Loss Functions [60.9237639890582]
  We introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion. Our method has been tested on KITTI Depth Completion Benchmark and achieved the state-of-the-art robustness performance in terms of MAE, IMAE, and IRMSE metrics.
  arXiv  Detail & Related papers  (2021-12-15T05:22:34Z)
• Boundary-induced and scene-aggregated network for monocular depth prediction [20.358133522462513]
  We propose the Boundary-induced and Scene-aggregated network (BS-Net) to predict the dense depth of a single RGB image. Several experimental results on the NYUD v2 dataset and xffthe iBims-1 dataset illustrate the state-of-the-art performance of the proposed approach.
  arXiv  Detail & Related papers  (2021-02-26T01:43:17Z)
• Efficient Depth Completion Using Learned Bases [94.0808155168311]
  We propose a new global geometry constraint for depth completion. By assuming depth maps often lay on low dimensional subspaces, a dense depth map can be approximated by a weighted sum of full-resolution principal depth bases.
  arXiv  Detail & Related papers  (2020-12-02T11:57:37Z)
• Deep Multi-view Depth Estimation with Predicted Uncertainty [11.012201499666503]
  We employ a dense-optical-flow network to compute correspondences and then triangulate the point cloud to obtain an initial depth map. To further increase the triangulation accuracy, we introduce a depth-refinement network (DRN) that optimize the initial depth map based on the image's contextual cues.
  arXiv  Detail & Related papers  (2020-11-19T00:22:09Z)
• Adaptive confidence thresholding for monocular depth estimation [83.06265443599521]
  We propose a new approach to leverage pseudo ground truth depth maps of stereo images generated from self-supervised stereo matching methods. The confidence map of the pseudo ground truth depth map is estimated to mitigate performance degeneration by inaccurate pseudo depth maps. Experimental results demonstrate superior performance to state-of-the-art monocular depth estimation methods.
  arXiv  Detail & Related papers  (2020-09-27T13:26:16Z)
• Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP [14.158132769768578]
  Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation algorithms have failed to keep their accuracy yielding a meter-level estimation error.
  arXiv  Detail & Related papers  (2020-08-12T08:07:55Z)
• Occlusion-Aware Depth Estimation with Adaptive Normal Constraints [85.44842683936471]
  We present a new learning-based method for multi-frame depth estimation from a color video. Our method outperforms the state-of-the-art in terms of depth estimation accuracy.
  arXiv  Detail & Related papers  (2020-04-02T07:10:45Z)

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.