Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation

• URL: http://arxiv.org/abs/2404.14908v1
• Date: Tue, 23 Apr 2024 10:51:15 GMT
• Title: Mining Supervision for Dynamic Regions in Self-Supervised Monocular Depth Estimation
• Authors: Hoang Chuong Nguyen, Tianyu Wang, Jose M. Alvarez, Miaomiao Liu,
• Abstract summary: Existing methods jointly estimate pixel-wise depth and motion, relying mainly on an image reconstruction loss. Dynamic regions1 remain a critical challenge for these methods due to the inherent ambiguity in depth and motion estimation. This paper proposes a self-supervised training framework exploiting pseudo depth labels for dynamic regions from training data.
• Score: 23.93080319283679
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper focuses on self-supervised monocular depth estimation in dynamic scenes trained on monocular videos. Existing methods jointly estimate pixel-wise depth and motion, relying mainly on an image reconstruction loss. Dynamic regions1 remain a critical challenge for these methods due to the inherent ambiguity in depth and motion estimation, resulting in inaccurate depth estimation. This paper proposes a self-supervised training framework exploiting pseudo depth labels for dynamic regions from training data. The key contribution of our framework is to decouple depth estimation for static and dynamic regions of images in the training data. We start with an unsupervised depth estimation approach, which provides reliable depth estimates for static regions and motion cues for dynamic regions and allows us to extract moving object information at the instance level. In the next stage, we use an object network to estimate the depth of those moving objects assuming rigid motions. Then, we propose a new scale alignment module to address the scale ambiguity between estimated depths for static and dynamic regions. We can then use the depth labels generated to train an end-to-end depth estimation network and improve its performance. Extensive experiments on the Cityscapes and KITTI datasets show that our self-training strategy consistently outperforms existing self/unsupervised depth estimation methods.

Related papers

• D$^3$epth: Self-Supervised Depth Estimation with Dynamic Mask in Dynamic Scenes [23.731667977542454]
  D$3$epth is a novel method for self-supervised depth estimation in dynamic scenes. It tackles the challenge of dynamic objects from two key perspectives. It consistently outperforms existing self-supervised monocular depth estimation baselines.
  arXiv  Detail & Related papers  (2024-11-07T16:07:00Z)
• 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)
• Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes [45.092076587934464]
  We present Manydepth2, to achieve precise depth estimation for both dynamic objects and static backgrounds. To tackle the challenges posed by dynamic content, we incorporate optical flow and coarse monocular depth to create a pseudo-static reference frame. This frame is then utilized to build a motion-aware cost volume in collaboration with the vanilla target frame.
  arXiv  Detail & Related papers  (2023-12-23T14:36:27Z)
• Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes [40.46121828229776]
  Dynamo-Depth is an approach that disambiguates dynamical motion by jointly learning monocular depth, 3D independent flow field, and motion segmentation from unlabeled monocular videos. Our proposed method achieves state-of-the-art performance on monocular depth estimation on Open and nuScenes with significant improvement in the depth of moving objects.
  arXiv  Detail & Related papers  (2023-10-29T03:24:16Z)
• Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering [93.94371335579321]
  We propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. Comprehensive experiments underscore our framework's superior generalization capabilities. Our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients.
  arXiv  Detail & Related papers  (2023-09-18T12:36:39Z)
• SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes [58.89295356901823]
  Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions. We introduce an external pretrained monocular depth estimation model for generating single-image depth prior. Our model can predict sharp and accurate depth maps, even when training from monocular videos of highly-dynamic scenes.
  arXiv  Detail & Related papers  (2022-11-07T16:17:47Z)
• SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning [53.78813049373321]
  We propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation. Our approach is useful for various applications such as mobile robot navigation and is applicable to diverse environments.
  arXiv  Detail & Related papers  (2022-03-10T12:28:42Z)
• X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation [69.9604394044652]
  We propose a novel method to improve the self-supervised training of monocular depth via cross-task knowledge distillation. During training, we utilize a pretrained semantic segmentation teacher network and transfer its semantic knowledge to the depth network. We extensively evaluate the efficacy of our proposed approach on the KITTI benchmark and compare it with the latest state of the art.
  arXiv  Detail & Related papers  (2021-10-24T19:47:14Z)
• DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes [68.38952377590499]
  We present a novel approach for estimating depth from a monocular camera as it moves through complex indoor environments. Our approach predicts absolute scale depth maps over the entire scene consisting of a static background and multiple moving people.
  arXiv  Detail & Related papers  (2021-08-12T09:12:39Z)
• Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues [24.743099160992937]
  We propose a novel self-supervised joint learning framework for depth estimation. The proposed framework outperforms the state-of-the-art(SOTA) on KITTI and Make3D datasets.
  arXiv  Detail & Related papers  (2020-06-17T13:56:59Z)

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.