Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

• URL: http://arxiv.org/abs/2106.05965v1
• Date: Thu, 10 Jun 2021 17:57:23 GMT
• Title: Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
• Authors: Kieran Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia
• Abstract summary: We introduce a method to estimate arbitrary, non-parametric distributions on SO(3). Our key idea is to represent the distributions implicitly, with a neural network that estimates the probability given the input image and a candidate pose. We achieve state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.
• Score: 47.31074799708132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses. To this end, we introduce a method to estimate arbitrary, non-parametric distributions on SO(3). Our key idea is to represent the distributions implicitly, with a neural network that estimates the probability given the input image and a candidate pose. Grid sampling or gradient ascent can be used to find the most likely pose, but it is also possible to evaluate the probability at any pose, enabling reasoning about symmetries and uncertainty. This is the most general way of representing distributions on manifolds, and to showcase the rich expressive power, we introduce a dataset of challenging symmetric and nearly-symmetric objects. We require no supervision on pose uncertainty -- the model trains only with a single pose per example. Nonetheless, our implicit model is highly expressive to handle complex distributions over 3D poses, while still obtaining accurate pose estimation on standard non-ambiguous environments, achieving state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.

Related papers

• ADen: Adaptive Density Representations for Sparse-view Camera Pose Estimation [17.097170273209333]
  Recovering camera poses from a set of images is a foundational task in 3D computer vision. Recent data-driven approaches aim to directly output camera poses, either through regressing the 6DoF camera poses or formulating rotation as a probability distribution. We propose ADen to unify the two frameworks by employing a generator and a discriminator.
  arXiv  Detail & Related papers  (2024-08-16T22:45:46Z)
• ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation [54.86887812687023]
  Most 3D-HPE methods rely on regression models, which assume a one-to-one mapping between inputs and outputs. We propose ManiPose, a novel manifold-constrained multi-hypothesis model capable of proposing multiple candidate 3D poses for each 2D input. Unlike previous multi-hypothesis approaches, our solution is completely supervised and does not rely on complex generative models.
  arXiv  Detail & Related papers  (2023-12-11T13:50:10Z)
• DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models [5.908471365011943]
  We propose emphDiffPose, a conditional diffusion model that predicts multiple hypotheses for a given input image. We show that DiffPose slightly improves upon the state of the art for multi-hypothesis pose estimation for simple poses and outperforms it by a large margin for highly ambiguous poses.
  arXiv  Detail & Related papers  (2022-11-29T18:55:13Z)
• Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels [23.01797447932351]
  We propose an automatic pose labeling scheme for RGB-D images. We train an ImplicitPDF model to estimate the likelihood of an orientation hypothesis given an RGB image. An efficient hierarchical sampling of the SO(3) manifold enables tractable generation of the complete set of symmetries.
  arXiv  Detail & Related papers  (2022-11-21T12:07:40Z)
• HandFlow: Quantifying View-Dependent 3D Ambiguity in Two-Hand Reconstruction with Normalizing Flow [73.7895717883622]
  We explicitly model the distribution of plausible reconstructions in a conditional normalizing flow framework. We show that explicit ambiguity modeling is better-suited for this challenging problem.
  arXiv  Detail & Related papers  (2022-10-04T15:42:22Z)
• 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)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• Probabilistic Modeling for Human Mesh Recovery [73.11532990173441]
  This paper focuses on the problem of 3D human reconstruction from 2D evidence. We recast the problem as learning a mapping from the input to a distribution of plausible 3D poses.
  arXiv  Detail & Related papers  (2021-08-26T17:55:11Z)

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.