Learning Positive-Incentive Point Sampling in Neural Implicit Fields for Object Pose Estimation

2026-02-23

AI Summary

This work addresses the challenge of pose estimation under highly occluded, novel-pose, or novel-geometry conditions, where existing neural implicit fields struggle to accurately map unobserved regions to canonical coordinates, leading to degraded performance. To overcome this limitation, the authors propose a novel approach that integrates an SO(3)-equivariant convolutional implicit network with a Positive-Incentive Point Sampling (PIPS) strategy. The former enables rotation-equivariant, point-wise attribute prediction at arbitrary spatial locations, while the latter dynamically selects the most informative sampling points based on input data to optimize training. This combination significantly enhances the model’s generalization and robustness in unobserved regions. The method achieves state-of-the-art results across three pose estimation benchmarks, demonstrating particularly strong performance under severe occlusion, novel poses, unseen geometries, and high noise levels.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Learning neural implicit fields of 3D shapes is a rapidly emerging field that enables shape representation at arbitrary resolutions. Due to the flexibility, neural implicit fields have succeeded in many research areas, including shape reconstruction, novel view image synthesis, and more recently, object pose estimation. Neural implicit fields enable learning dense correspondences between the camera space and the object's canonical space-including unobserved regions in camera space-significantly boosting object pose estimation performance in challenging scenarios like highly occluded objects and novel shapes. Despite progress, predicting canonical coordinates for unobserved camera-space regions remains challenging due to the lack of direct observational signals. This necessitates heavy reliance on the model's generalization ability, resulting in high uncertainty. Consequently, densely sampling points across the entire camera space may yield inaccurate estimations that hinder the learning process and compromise performance. To alleviate this problem, we propose a method combining an SO(3)-equivariant convolutional implicit network and a positive-incentive point sampling (PIPS) strategy. The SO(3)-equivariant convolutional implicit network estimates point-level attributes with SO(3)-equivariance at arbitrary query locations, demonstrating superior performance compared to most existing baselines. The PIPS strategy dynamically determines sampling locations based on the input, thereby boosting the network's accuracy and training efficiency. Our method outperforms the state-of-the-art on three pose estimation datasets. Notably, it demonstrates significant improvements in challenging scenarios, such as objects captured with unseen pose, high occlusion, novel geometry, and severe noise.

Problem

• neural implicit fields
• object pose estimation
• unobserved regions
• canonical coordinates
• point sampling

Innovation

• SO(3)-equivariant
• neural implicit fields
• positive-incentive point sampling
• object pose estimation
• dynamic sampling