Active Depth Estimation: Stability Analysis and its Applications
- URL: http://arxiv.org/abs/2003.07137v1
- Date: Mon, 16 Mar 2020 12:12:24 GMT
- Title: Active Depth Estimation: Stability Analysis and its Applications
- Authors: Romulo T. Rodrigues, Pedro Miraldo, Dimos V. Dimarogonas, and A. Pedro
Aguiar
- Abstract summary: This paper focuses on the theoretical properties of the Structure-from-Motion (SfM) scheme.
The term incremental stands for estimating the 3D structure of the scene over a chronological sequence of image frames.
By analyzing the convergence of the estimator using the Lyapunov theory, we relax the constraints on the projection of the 3D point in the image plane.
- Score: 18.582561853987034
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recovering the 3D structure of the surrounding environment is an essential
task in any vision-controlled Structure-from-Motion (SfM) scheme. This paper
focuses on the theoretical properties of the SfM, known as the incremental
active depth estimation. The term incremental stands for estimating the 3D
structure of the scene over a chronological sequence of image frames. Active
means that the camera actuation is such that it improves estimation
performance. Starting from a known depth estimation filter, this paper presents
the stability analysis of the filter in terms of the control inputs of the
camera. By analyzing the convergence of the estimator using the Lyapunov
theory, we relax the constraints on the projection of the 3D point in the image
plane when compared to previous results. Nonetheless, our method is capable of
dealing with the cameras' limited field-of-view constraints. The main results
are validated through experiments with simulated data.
Related papers
- KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction [58.04846444985808]
This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints.
With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point.
arXiv Detail & Related papers (2024-09-09T08:08:05Z) - DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and
Depth from Monocular Videos [76.01906393673897]
We propose a self-supervised method to jointly learn 3D motion and depth from monocular videos.
Our system contains a depth estimation module to predict depth, and a new decomposed object-wise 3D motion (DO3D) estimation module to predict ego-motion and 3D object motion.
Our model delivers superior performance in all evaluated settings.
arXiv Detail & Related papers (2024-03-09T12:22:46Z) - OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
We propose an OccNeRF method for training occupancy networks without 3D supervision.
We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range.
For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
arXiv Detail & Related papers (2023-12-14T18:58:52Z) - iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching [14.737266480464156]
We present a method named iComMa to address the 6D camera pose estimation problem in computer vision.
We propose an efficient method for accurate camera pose estimation by inverting 3D Gaussian Splatting (3DGS)
arXiv Detail & Related papers (2023-12-14T15:31:33Z) - Instance-aware Multi-Camera 3D Object Detection with Structural Priors
Mining and Self-Boosting Learning [93.71280187657831]
Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field.
We propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector.
arXiv Detail & Related papers (2023-12-13T09:24:42Z) - Fully Self-Supervised Depth Estimation from Defocus Clue [79.63579768496159]
We propose a self-supervised framework that estimates depth purely from a sparse focal stack.
We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions.
arXiv Detail & Related papers (2023-03-19T19:59:48Z) - DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and
Parallax [0.0]
Current self-supervised monocular depth estimation methods are mostly based on estimating a rigid-body motion representing camera motion.
We propose DepthP+P, a method that learns to estimate outputs in metric scale by following the traditional planar parallax paradigm.
arXiv Detail & Related papers (2023-01-05T14:53:21Z) - Monocular 3D Object Detection with Depth from Motion [74.29588921594853]
We take advantage of camera ego-motion for accurate object depth estimation and detection.
Our framework, named Depth from Motion (DfM), then uses the established geometry to lift 2D image features to the 3D space and detects 3D objects thereon.
Our framework outperforms state-of-the-art methods by a large margin on the KITTI benchmark.
arXiv Detail & Related papers (2022-07-26T15:48:46Z) - RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust
Correspondence Field Estimation and Pose Optimization [46.144194562841435]
We propose a framework based on a recurrent neural network (RNN) for object pose refinement.
The problem is formulated as a non-linear least squares problem based on the estimated correspondence field.
The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover accurate object poses.
arXiv Detail & Related papers (2022-03-24T06:24:55Z) - DiffPoseNet: Direct Differentiable Camera Pose Estimation [11.941057800943653]
We introduce a network NFlowNet, for normal flow estimation which is used to enforce robust and direct constraints.
We perform extensive qualitative and quantitative evaluation of the proposed DiffPoseNet's sensitivity to noise and its generalization across datasets.
arXiv Detail & Related papers (2022-03-21T17:54:30Z) - A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous
Cars [0.0]
In this paper, the YOLOv3 approach is utilized beside an OpenCV tracker to elicit features from an image.
To obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation filter.
A switching method in the form of switching estimation error covariance is proposed to enhance the robust performance of the SDRE filter.
arXiv Detail & Related papers (2021-03-24T18:21:29Z)
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.