Active Event Alignment for Monocular Distance Estimation

• URL: http://arxiv.org/abs/2410.22280v1
• Date: Tue, 29 Oct 2024 17:34:01 GMT
• Title: Active Event Alignment for Monocular Distance Estimation
• Authors: Nan Cai, Pia Bideau,
• Abstract summary: Event cameras provide a natural and data efficient representation of visual information. We propose a behavior driven approach for object-wise distance estimation from event camera data.
• Score: 2.9189383211046014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event cameras provide a natural and data efficient representation of visual information, motivating novel computational strategies towards extracting visual information. Inspired by the biological vision system, we propose a behavior driven approach for object-wise distance estimation from event camera data. This behavior-driven method mimics how biological systems, like the human eye, stabilize their view based on object distance: distant objects require minimal compensatory rotation to stay in focus, while nearby objects demand greater adjustments to maintain alignment. This adaptive strategy leverages natural stabilization behaviors to estimate relative distances effectively. Unlike traditional vision algorithms that estimate depth across the entire image, our approach targets local depth estimation within a specific region of interest. By aligning events within a small region, we estimate the angular velocity required to stabilize the image motion. We demonstrate that, under certain assumptions, the compensatory rotational flow is inversely proportional to the object's distance. The proposed approach achieves new state-of-the-art accuracy in distance estimation - a performance gain of 16% on EVIMO2. EVIMO2 event sequences comprise complex camera motion and substantial variance in depth of static real world scenes.

Related papers

• Event-based Visual Deformation Measurement [76.25283405575108]
  Visual Deformation Measurement aims to recover dense deformation fields by tracking surface motion from camera observations.<n>Traditional image-based methods rely on minimal inter-frame motion to constrain the correspondence search space.<n>We propose an event-frame fusion framework that exploits events for temporally dense motion cues and frames for spatially dense precise estimation.
  arXiv  Detail & Related papers  (2026-02-16T01:04:48Z)
• GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion [7.588468985212172]
  Generalizing metric monocular depth estimation presents a significant challenge due to its ill-posed nature. We propose a novel canonical representation that maintains consistency across varied camera setups. We also propose a novel architecture that adaptively and probabilistically fuses depths estimated via object size and vertical image position cues.
  arXiv  Detail & Related papers  (2024-12-08T22:04:34Z)
• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point. It tackles the challenges posed by spatial sparsity and motion sensitivity in events. It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras [33.81592783496106]
  Event-based visual odometry aims at solving tracking and mapping sub-problems in parallel. We build an event-based stereo visual-inertial odometry system on top of our previous direct pipeline Event-based Stereo Visual Odometry.
  arXiv  Detail & Related papers  (2024-10-12T05:35:27Z)
• Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera [19.204896246140155]
  Event cameras possess remarkable attributes such as high dynamic range, low latency, and resilience against motion blur. We propose a line-based robust pose estimation and tracking method for planar or non-planar objects using an event camera.
  arXiv  Detail & Related papers  (2024-08-06T14:36:43Z)
• VICAN: Very Efficient Calibration Algorithm for Large Camera Networks [49.17165360280794]
  We introduce a novel methodology that extends Pose Graph Optimization techniques. We consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step. Our framework retains compatibility with traditional PGO solvers, but its efficacy benefits from a custom-tailored optimization scheme.
  arXiv  Detail & Related papers  (2024-03-25T17:47:03Z)
• Generalizing Event-Based Motion Deblurring in Real-World Scenarios [62.995994797897424]
  Event-based motion deblurring has shown promising results by exploiting low-latency events. We propose a scale-aware network that allows flexible input spatial scales and enables learning from different temporal scales of motion blur. A two-stage self-supervised learning scheme is then developed to fit real-world data distribution.
  arXiv  Detail & Related papers  (2023-08-11T04:27:29Z)
• On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing [69.34740063574921]
  This paper presents a methodology for generating event-based vision datasets from optimal landing trajectories. We construct sequences of photorealistic images of the lunar surface with the Planet and Asteroid Natural Scene Generation Utility. We demonstrate that the pipeline can generate realistic event-based representations of surface features by constructing a dataset of 500 trajectories.
  arXiv  Detail & Related papers  (2023-08-01T09:14:20Z)
• ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild [57.37891682117178]
  We present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence from pairwise optical flow. A novel neural network architecture is proposed for processing irregular point trajectory data. Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories.
  arXiv  Detail & Related papers  (2022-07-19T09:19:45Z)
• Event-based Motion Segmentation with Spatio-Temporal Graph Cuts [51.17064599766138]
  We have developed a method to identify independently objects acquired with an event-based camera. The method performs on par or better than the state of the art without having to predetermine the number of expected moving objects.
  arXiv  Detail & Related papers  (2020-12-16T04:06:02Z)
• End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [81.66569124029313]
  We propose a camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field.
  arXiv  Detail & Related papers  (2020-06-07T08:18:31Z)
• Maximum Likelihood Speed Estimation of Moving Objects in Video Signals [0.12891210250935145]
  In most realistic scenarios, the projection of a framed object of interest onto the image plane is likely to be affected by dynamic changes. The proposed method is composed of a sequence of pre-processing operations, that aim to reduce or neglect perspetival effects affecting the objects of interest. The ML estimation method represents, indeed, a consolidated statistical tool that may be exploited to obtain reliable results.
  arXiv  Detail & Related papers  (2020-03-10T17:56:50Z)

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.