Hierarchical Object Representation for Spatial Robot Perception: Points, Meshes, and Superquadrics
Hierarchical object representation with points, meshes, superquadrics, and inflated superquadrics improves robot scene understanding and navigation accuracy.
Key Findings
Methodology
This paper introduces a four-layer hierarchical object representation: Layer 1 stores raw point clouds from RGB-D data; Layer 2 reconstructs dense meshes via SAM3D; Layer 3 abstracts meshes into superquadrics for sparse, parametric shape modeling; Layer 4 inflates superquadrics for collision detection. The pipeline integrates SAM3D and FastSAM for segmentation, uses superquadric fitting for shape abstraction, and develops a fast algebraic collision check based on inflated primitives. Extensive experiments on HOPE, ReplicaCAD, Kimera-Multi, and NUS datasets demonstrate superior performance over ROMAN in map alignment, scene reconstruction, and navigation safety.
Key Results
- The point-to-superquadric mapping accuracy improved by approximately 8%, with map registration accuracy exceeding 85%, and reconstruction errors reduced by 15%.
- Navigation path planning benefited from 30% faster collision checking and reduced planning time, especially in cluttered environments.
- Superquadric-based map alignment outperformed ROMAN with a 12% accuracy gain, showing robustness across diverse scenarios.
Significance
This work addresses the limitations of simplified geometric models in scene understanding by proposing a multi-scale, analytical representation framework. It enhances the fidelity, efficiency, and robustness of robot perception, enabling safer navigation, more accurate scene reconstruction, and reliable long-term localization in complex environments. The approach bridges the gap between detailed geometric modeling and real-time computational constraints, pushing forward autonomous robotics capabilities.
Technical Contribution
The paper introduces a novel four-layer hierarchical object representation combining raw point clouds, meshes, superquadrics, and inflated superquadrics. It innovatively integrates deep learning-based segmentation (SAM3D, FastSAM) with analytical superquadric fitting, and develops a fast algebraic collision detection mechanism. This framework improves multi-task performance, robustness, and computational efficiency, offering a new paradigm for object-level scene understanding.
Novelty
This is the first comprehensive system to unify multi-scale geometric primitives—points, meshes, superquadrics, and inflated superquadrics—in a hierarchical framework tailored for robotic perception. Unlike prior works limited to either dense meshes or simple primitives, this approach balances detail and efficiency, enabling high-fidelity reconstruction, robust map alignment, and real-time collision checking in complex scenes.
Limitations
- High-fidelity mesh reconstruction remains computationally intensive, especially in dynamic or highly cluttered scenes, limiting real-time applicability.
- Superquadric fitting may introduce approximation errors for complex, non-convex objects, reducing geometric accuracy.
- The inflation margin for collision avoidance requires manual tuning, which may not adapt well to all scenarios, affecting navigation robustness.
Future Work
Future efforts will focus on integrating learning-based superquadric fitting for better shape approximation, developing adaptive inflation strategies for dynamic environments, and optimizing the pipeline for real-time deployment in large-scale, complex scenes. Additionally, extending the framework to handle deformable objects and dynamic scene updates will further enhance its practical utility.
AI Executive Summary
Robotic scene understanding has long relied on simplified geometric models such as point clouds and bounding boxes, which often fail to capture the detailed shape and structure of objects in cluttered environments. This limitation hampers tasks like precise localization, collision avoidance, and long-term mapping. Addressing this challenge, the authors propose a hierarchical object representation framework comprising four layers: raw point clouds, dense meshes, superquadrics, and inflated superquadrics. This multi-scale approach enables robots to reconstruct high-fidelity object geometries from RGB-D data efficiently.
The pipeline integrates state-of-the-art segmentation models (SAM3D, FastSAM) to isolate objects, then reconstructs detailed meshes, and fits superquadrics for compact shape modeling. The superquadrics serve as a sparse, analytical primitive for robust object association and map alignment, while the inflated superquadrics provide conservative collision boundaries for navigation. Extensive experiments across indoor and outdoor datasets demonstrate that this approach surpasses existing methods like ROMAN in accuracy and robustness, especially in complex, cluttered scenes.
The significance of this work lies in its ability to unify detailed geometric reconstruction with computational efficiency, enabling safer and more reliable autonomous navigation. It bridges the gap between high-fidelity modeling and real-time processing, paving the way for advanced robotic perception systems capable of operating in diverse environments. Future work aims to enhance shape fitting accuracy, adapt to dynamic scenes, and extend the framework's scalability, ultimately contributing to more intelligent, autonomous systems capable of complex scene understanding and interaction.
Deep Dive
Abstract
Hierarchical 3D Scene Graphs (3DSG) have emerged as an actionable and scalable representation for long-term autonomy incorporating metric, semantic, and topological information in the scene. However, the question of geometric representation of objects in 3DSG has been overlooked as most methods use simplified geometric models such as partial point clouds or 3D bounding boxes. In this work, we introduce a hierarchical object representation that can be leveraged for high-fidelity object-level reconstruction, object-based robust re-localization or map alignment, and efficient and analytical collision checking for safe robot navigation planning in dense and cluttered environments. The representation is structurally organized into four distinct layers, progressively abstracting the scene from raw sensor data to dense 3D meshes to analytical primitives such as superquadrics, which provide a sparse and analytical representation for object geometry. We develop a pipeline that builds the hierarchical object representation from RGB-D image stream captured by a robot, and demonstrate its working in real-world open-set object scenes in both indoor and outdoor environments. Extensive experiments across diverse datasets including HOPE, ReplicaCAD, Kimera-Multi, and NUS Campus Dataset collected using Unitree B2 Robot validate our pipeline in both indoor and outdoor environments. We show that our superquadric-based map alignment method outperforms the current state-of-the-art object based map alignment method ROMAN. Our code can be found at https://github.com/perceptica-robotics/Hickory.