DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for
Robust Real-Time Feature Matching
- URL: http://arxiv.org/abs/2007.16005v1
- Date: Fri, 31 Jul 2020 12:18:18 GMT
- Title: DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for
Robust Real-Time Feature Matching
- Authors: Patrick Ruhkamp and Ruiqi Gong and Nassir Navab and Benjamin Busam
- Abstract summary: We present the lightweight pipeline DynaMiTe, which is agnostic to the descriptor input and leverages spatial-temporal cues with efficient statistical measures.
DynaMiTe achieves superior results both in terms of matching accuracy and camera pose estimation with high frame rates, outperforming state-of-the-art matching methods.
- Score: 47.72468932196169
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Feature based visual odometry and SLAM methods require accurate and fast
correspondence matching between consecutive image frames for precise camera
pose estimation in real-time. Current feature matching pipelines either rely
solely on the descriptive capabilities of the feature extractor or need
computationally complex optimization schemes. We present the lightweight
pipeline DynaMiTe, which is agnostic to the descriptor input and leverages
spatial-temporal cues with efficient statistical measures. The theoretical
backbone of the method lies within a probabilistic formulation of feature
matching and the respective study of physically motivated constraints. A
dynamically adaptable local motion model encapsulates groups of features in an
efficient data structure. Temporal constraints transfer information of the
local motion model across time, thus additionally reducing the search space
complexity for matching. DynaMiTe achieves superior results both in terms of
matching accuracy and camera pose estimation with high frame rates,
outperforming state-of-the-art matching methods while being computationally
more efficient.
Related papers
- MATEY: multiscale adaptive foundation models for spatiotemporal physical systems [2.7767126393602726]
We propose two adaptive tokenization schemes that dynamically adjust patch sizes based on local features.
We evaluate the performance of a proposed multiscale adaptive model, MATEY, in a sequence of experiments.
We also demonstrate fine-tuning tasks featuring different physics that models pretrained on PDE data.
arXiv Detail & Related papers (2024-12-29T22:13:16Z) - 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) - Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting [16.782154479264126]
Predicting backbone-temporal traffic flow presents challenges due to complex interactions between temporal factors.
Existing approaches address these dimensions in isolation, neglecting their critical interdependencies.
In this paper, we introduce Sanonymous-Temporal Unitized Unitized Cell (ASTUC), a unified framework designed to capture both spatial and temporal dependencies.
arXiv Detail & Related papers (2024-11-14T07:34:31Z) - Event-Aided Time-to-Collision Estimation for Autonomous Driving [28.13397992839372]
We present a novel method that estimates the time to collision using a neuromorphic event-based camera.
The proposed algorithm consists of a two-step approach for efficient and accurate geometric model fitting on event data.
Experiments on both synthetic and real data demonstrate the effectiveness of the proposed method.
arXiv Detail & Related papers (2024-07-10T02:37:36Z) - Motion-Aware Video Frame Interpolation [49.49668436390514]
We introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames.
It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, but also effectively reduces the required computational cost and complexity.
arXiv Detail & Related papers (2024-02-05T11:00:14Z) - Learning Unnormalized Statistical Models via Compositional Optimization [73.30514599338407]
Noise-contrastive estimation(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise.
In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models.
arXiv Detail & Related papers (2023-06-13T01:18:16Z) - Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in
Driving Scenes [82.4186966781934]
We introduce a simple, efficient, and effective two-stage detector, termed as Ret3D.
At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules.
With negligible extra overhead, Ret3D achieves the state-of-the-art performance.
arXiv Detail & Related papers (2022-08-18T03:48:58Z) - Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem
Formulation [53.850686395708905]
Event-based cameras record an asynchronous stream of per-pixel brightness changes.
In this paper, we focus on single-layer architectures for representation learning from event data.
We show improvements of up to 9 % in the recognition accuracy compared to the state-of-the-art methods.
arXiv Detail & Related papers (2020-09-23T10:40:03Z) - Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low
Latency [23.443265839365054]
Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency)
This paper aims to fill the performance-efficiency gap with an enhancement applied to feature-based VSLAM.
arXiv Detail & Related papers (2020-01-03T03:50:54Z)
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.