OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow

• URL: http://arxiv.org/abs/2102.00364v1
• Date: Sun, 31 Jan 2021 03:30:31 GMT
• Title: OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow
• Authors: Lingtong Kong, Xiaohang Yang, Jie Yang
• Abstract summary: Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm. We propose a lightweight yet efficient optical flow network, named OAS-Net, for accurate optical flow. Experiments on Sintel and KITTI datasets demonstrate the effectiveness of proposed approaches.
• Score: 4.42249337449125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key process is to adopt warped target feature based on previous flow prediction to correlate with source feature for building 3D matching cost volume. However, the warping operation can lead to troublesome ghosting problem that results in ambiguity. Moreover, occluded areas are treated equally with non occluded regions in most existing works, which may cause performance degradation. To deal with these challenges, we propose a lightweight yet efficient optical flow network, named OAS-Net (occlusion aware sampling network) for accurate optical flow. First, a new sampling based correlation layer is employed without noisy warping operation. Second, a novel occlusion aware module is presented to make raw cost volume conscious of occluded regions. Third, a shared flow and occlusion awareness decoder is adopted for structure compactness. Experiments on Sintel and KITTI datasets demonstrate the effectiveness of proposed approaches.

Related papers

• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• Rethink Predicting the Optical Flow with the Kinetics Perspective [1.7901503554839604]
  Optical flow estimation is one of the fundamental tasks in low-level computer vision. From the apparent aspect, the optical flow can be viewed as the correlation between the pixels in consecutive frames. We propose a method combining the apparent and kinetics information from this motivation.
  arXiv  Detail & Related papers  (2024-05-21T05:47:42Z)
• Sensor-Guided Optical Flow [53.295332513139925]
  This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy on known or unseen domains. We show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms.
  arXiv  Detail & Related papers  (2021-09-30T17:59:57Z)
• A Cascaded Zoom-In Network for Patterned Fabric Defect Detection [8.789819609485225]
  We propose a two-step Cascaded Zoom-In Network (CZI-Net) for patterned fabric defect detection. In the CZI-Net, the Aggregated HOG (A-HOG) and SIFT features are used to instead of simple convolution filters for feature extraction. Experiments based on real-world datasets are implemented and demonstrate that our proposed method is not only computationally simple but also with high detection accuracy.
  arXiv  Detail & Related papers  (2021-08-15T15:29:26Z)
• Self-Guided Instance-Aware Network for Depth Completion and Enhancement [6.319531161477912]
  Existing methods directly interpolate the missing depth measurements based on pixel-wise image content and the corresponding neighboring depth values. We propose a novel self-guided instance-aware network (SG-IANet) that utilize self-guided mechanism to extract instance-level features that is needed for depth restoration.
  arXiv  Detail & Related papers  (2021-05-25T19:41:38Z)
• Occlusion Guided Scene Flow Estimation on 3D Point Clouds [4.518012967046983]
  3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space.
  arXiv  Detail & Related papers  (2020-11-30T15:22:03Z)
• LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation [99.19322851246972]
  We introduce LiteFlowNet3, a deep network consisting of two specialized modules to address the problem of optical flow estimation. LiteFlowNet3 not only achieves promising results on public benchmarks but also has a small model size and a fast runtime.
  arXiv  Detail & Related papers  (2020-07-18T03:30:39Z)
• What Matters in Unsupervised Optical Flow [51.45112526506455]
  We compare and analyze a set of key components in unsupervised optical flow. We construct a number of novel improvements to unsupervised flow models. We present a new unsupervised flow technique that significantly outperforms the previous state-of-the-art.
  arXiv  Detail & Related papers  (2020-06-08T19:36:26Z)
• Resolution Adaptive Networks for Efficient Inference [53.04907454606711]
  We propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations. High-resolution paths in the network maintain the capability to recognize the "hard" samples.
  arXiv  Detail & Related papers  (2020-03-16T16:54:36Z)
• Selective Convolutional Network: An Efficient Object Detector with Ignoring Background [28.591619763438054]
  We introduce an efficient object detector called Selective Convolutional Network (SCN), which selectively calculates only on the locations that contain meaningful and conducive information. To solve it, we design an elaborate structure with negligible overheads to guide the network where to look next.
  arXiv  Detail & Related papers  (2020-02-04T10:07:01Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

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.