Globally Optimal Pose from Orthographic Silhouettes

2026-04-10

AI Summary

This work addresses the problem of estimating the pose of a known 3D shape from an unoccluded orthographic silhouette without relying on feature point correspondences. The method leverages the continuity of silhouette area along rotational trajectories to construct a precomputed silhouette signature response surface and introduces the aspect ratio of a fitted ellipse as a global shape signature. This enables an efficient, resolution-guided branch-and-bound search over the rotation space. To the best of our knowledge, this is the first approach capable of achieving globally optimal pose estimation using only silhouettes for arbitrary shapes, including non-convex and high-genus geometries. Experiments on both synthetic and real-world data demonstrate that the proposed method significantly outperforms existing techniques in both accuracy and computational efficiency.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

We solve the problem of determining the pose of known shapes in $\mathbb{R}^3$ from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches. Code, data, and supplementary in: https://agnivsen.github.io/pose-from-silhouette/

Problem

• pose estimation
• silhouette
• global optimality
• orthographic projection
• 3D shape

Innovation

• globally optimal pose
• silhouette signature
• rotation space continuity
• orthographic silhouette
• shape-agnostic pose estimation