Object Pose Estimation with Statistical Guarantees: Conformal Keypoint
Detection and Geometric Uncertainty Propagation
- URL: http://arxiv.org/abs/2303.12246v1
- Date: Wed, 22 Mar 2023 00:55:53 GMT
- Title: Object Pose Estimation with Statistical Guarantees: Conformal Keypoint
Detection and Geometric Uncertainty Propagation
- Authors: Heng Yang, Marco Pavone
- Abstract summary: The two-stage object pose estimation first detects semantic keypoints on the image then estimates the 6D pose by minimizing reprojection errors.
Existing techniques offer no provable guarantees of uncertainty of estimation.
We develop RANdom SA averaGing (RANSAG) to compute an average pose and apply the worst-case error bounds.
- Score: 38.54398084892807
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The two-stage object pose estimation paradigm first detects semantic
keypoints on the image and then estimates the 6D pose by minimizing
reprojection errors. Despite performing well on standard benchmarks, existing
techniques offer no provable guarantees on the quality and uncertainty of the
estimation. In this paper, we inject two fundamental changes, namely conformal
keypoint detection and geometric uncertainty propagation, into the two-stage
paradigm and propose the first pose estimator that endows an estimation with
provable and computable worst-case error bounds. On one hand, conformal
keypoint detection applies the statistical machinery of inductive conformal
prediction to convert heuristic keypoint detections into circular or elliptical
prediction sets that cover the groundtruth keypoints with a user-specified
marginal probability (e.g., 90%). Geometric uncertainty propagation, on the
other, propagates the geometric constraints on the keypoints to the 6D object
pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the
groundtruth pose with the same probability. The PURSE, however, is a nonconvex
set that does not directly lead to estimated poses and uncertainties.
Therefore, we develop RANdom SAmple averaGing (RANSAG) to compute an average
pose and apply semidefinite relaxation to upper bound the worst-case errors
between the average pose and the groundtruth. On the LineMOD Occlusion dataset
we demonstrate: (i) the PURSE covers the groundtruth with valid probabilities;
(ii) the worst-case error bounds provide correct uncertainty quantification;
and (iii) the average pose achieves better or similar accuracy as
representative methods based on sparse keypoints.
Related papers
- Distributional Shift-Aware Off-Policy Interval Estimation: A Unified
Error Quantification Framework [8.572441599469597]
We study high-confidence off-policy evaluation in the context of infinite-horizon Markov decision processes.
The objective is to establish a confidence interval (CI) for the target policy value using only offline data pre-collected from unknown behavior policies.
We show that our algorithm is sample-efficient, error-robust, and provably convergent even in non-linear function approximation settings.
arXiv Detail & Related papers (2023-09-23T06:35:44Z) - Discretization-Induced Dirichlet Posterior for Robust Uncertainty
Quantification on Regression [17.49026509916207]
Uncertainty quantification is critical for deploying deep neural networks (DNNs) in real-world applications.
For vision regression tasks, current AuxUE designs are mainly adopted for aleatoric uncertainty estimates.
We propose a generalized AuxUE scheme for more robust uncertainty quantification on regression tasks.
arXiv Detail & Related papers (2023-08-17T15:54:11Z) - Toward Reliable Human Pose Forecasting with Uncertainty [51.628234388046195]
We develop an open-source library for human pose forecasting, including multiple models, supporting several datasets.
We devise two types of uncertainty in the problem to increase performance and convey better trust.
arXiv Detail & Related papers (2023-04-13T17:56:08Z) - Ki-Pode: Keypoint-based Implicit Pose Distribution Estimation of Rigid
Objects [1.209625228546081]
We propose a novel pose distribution estimation method.
An implicit formulation of the probability distribution over object pose is derived from an intermediary representation of an object as a set of keypoints.
The method has been evaluated on the task of rotation distribution estimation on the YCB-V and T-LESS datasets.
arXiv Detail & Related papers (2022-09-20T11:59:05Z) - Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose
Estimation [70.32536356351706]
We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations.
We derive suitable measures to quantify prediction uncertainty at both pose and joint level.
We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
arXiv Detail & Related papers (2022-03-29T07:14:58Z) - NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural
Networks [151.03112356092575]
We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution.
We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
arXiv Detail & Related papers (2022-02-07T12:30:45Z) - Dense Uncertainty Estimation via an Ensemble-based Conditional Latent
Variable Model [68.34559610536614]
We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model.
We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation.
Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
arXiv Detail & Related papers (2021-11-22T08:54:10Z) - Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal
Estimation [25.003116148843525]
Surface normal estimation from a single image is an important task in 3D scene understanding.
In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction.
We present a novel decoder framework where pixel-wise perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty.
arXiv Detail & Related papers (2021-09-20T23:30:04Z) - Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.
These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem.
We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
arXiv Detail & Related papers (2020-12-10T23:28:36Z) - Labels Are Not Perfect: Improving Probabilistic Object Detection via
Label Uncertainty [12.531126969367774]
We leverage our previously proposed method for estimating uncertainty inherent in ground truth bounding box parameters.
Experimental results on the KITTI dataset show that our method surpasses both the baseline model and the models based on simple uncertaintys by up to 3.6% in terms of Average Precision.
arXiv Detail & Related papers (2020-08-10T14:49:49Z)
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.