NoPA: Non-Parametric Online 3D Scene Graph Generation

Hugging Face Daily Papers Papers
3d-scene-graph non-parametric real-time kernel-density-estimate object-representation computer-vision robotics

Summary

NoPA introduces a non-parametric distribution-based approach for real-time 3D scene graph generation, preserving geometric details using kernel density estimates and particle-based object representation, substantially outperforming current methods.

Classic 3D scene graph generation approaches fail to work in real-time due to the heavy computational cost of environment mapping and the need to generate intermediate point-cloud representations. To alleviate this issue, a recent work eschews point clouds in favor of a lightweight Gaussian distribution for each object. This approximation drastically speeds up inference and enables real-time 3D scene graph generation. However, the representation has two key weaknesses. 1) Each object is approximated by a single 3D Gaussian, which causes a severe loss of 3D geometric detail. 2) The discrepancy between this approximation and the true object geometry exacerbates the inaccurate merging of object candidates during online inference. To address these issues, we propose NoPA, which represents each object as a separate non-parametric distribution. This formulation retains 3D geometric information while preserving real-time inference of the parametric Gaussian formulation. To build upon our novel object representation, we propose a tailored merging strategy to recover coherent object instances. Specifically, we leverage maximum mean discrepancy on kernel density estimates to enable robust merging of object candidates during online exploration while minimizing added computational complexity. The key is to maintain a fixed particle set per object. Furthermore, to rectify the relation loss caused by misclassified objects, NoPA propagates relationships between objects with high affinity. Experiments show that NoPA substantially outperforms current methods without sacrificing real-time inference speed.
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Paper page - NoPA: Non-Parametric Online 3D Scene Graph Generation

Source: https://huggingface.co/papers/2607.00529

Abstract

NoPA introduces a non-parametric distribution-based approach for real-time 3D scene graph generation that preserves geometric details while maintaining computational efficiency through kernel density estimates and particle-based object representation.

Classic3D scene graph generationapproaches fail to work in real-time due to the heavy computational cost of environment mapping and the need to generate intermediate point-cloud representations. To alleviate this issue, a recent work eschews point clouds in favor of a lightweightGaussian distributionfor each object. This approximation drastically speeds up inference and enables real-time3D scene graph generation. However, the representation has two key weaknesses. 1) Each object is approximated by a single 3D Gaussian, which causes a severe loss of 3D geometric detail. 2) The discrepancy between this approximation and the true object geometry exacerbates the inaccurate merging of object candidates during online inference. To address these issues, we propose NoPA, which represents each object as a separatenon-parametric distribution. This formulation retains 3D geometric information while preservingreal-time inferenceof the parametric Gaussian formulation. To build upon our novel object representation, we propose a tailored merging strategy to recover coherent object instances. Specifically, we leveragemaximum mean discrepancyonkernel density estimatesto enable robust merging of object candidates during online exploration while minimizing added computational complexity. The key is to maintain a fixedparticle setper object. Furthermore, to rectify the relation loss caused by misclassified objects, NoPA propagates relationships between objects with high affinity. Experiments show that NoPA substantially outperforms current methods without sacrificingreal-time inferencespeed.

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