VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding
VEOcc uses a voxel-centric framework for online semantic occupancy prediction, excelling on Occ-ScanNet.
Key Findings
Methodology
VEOcc employs a voxel-centric recursive perception-and-assimilation paradigm, eliminating the need for initial scale estimation. Its core is a Spatio-Temporal-Aware Online Update Strategy, integrating Cross-Temporal Logit Aggregation, Reliability-Aware Confidence Modulation, and Confidence-Driven Incremental State Update.
Key Results
- On Occ-ScanNet, VEOcc achieved 64.55 IoU and 55.49 mIoU, surpassing SplatSSC by 1.72 IoU and 3.66 mIoU.
- On EmbodiedOcc-ScanNet, VEOcc reached 62.21 IoU and 53.00 mIoU, significantly outperforming RoboOcc.
- Zero-shot evaluations on self-collected video sequences confirmed its robust generalization capability.
Significance
VEOcc is significant in academia and industry, addressing existing methods' shortcomings in structural boundary fidelity and scene-size prior dependency, offering a more efficient solution for autonomous exploration.
Technical Contribution
VEOcc introduces a voxel-centric representation, offering more uniform spatial coverage and structural boundary fidelity compared to existing Gaussian-centric methods. Its online update strategy effectively integrates multi-view noise, ensuring consistent global semantic maps.
Novelty
VEOcc is the first to propose a voxel-centric framework without predefined scene size, significantly enhancing the flexibility and accuracy of online semantic occupancy prediction.
Limitations
- In complex scenes, VEOcc may be affected by noise and uncertainty, leading to reduced prediction accuracy.
- High computational resource requirements may limit its application on resource-constrained devices.
Future Work
Future work can explore applications in resource-constrained environments, optimize computational efficiency, and enhance generalization in more complex scenarios.
AI Executive Summary
VEOcc is a novel voxel-centric online semantic occupancy prediction framework designed to address the shortcomings of existing Gaussian-centric methods in structural boundary fidelity and scene-size prior dependency. By eliminating the need for initial scale estimation, VEOcc achieves efficient map expansion. Its core is a Spatio-Temporal-Aware Online Update Strategy, integrating Cross-Temporal Logit Aggregation, Reliability-Aware Confidence Modulation, and Confidence-Driven Incremental State Update, setting new performance benchmarks on Occ-ScanNet and EmbodiedOcc-ScanNet.
In experiments, VEOcc achieved 64.55 IoU and 55.49 mIoU on Occ-ScanNet, significantly surpassing existing Gaussian-centric baseline methods. On EmbodiedOcc-ScanNet, VEOcc also outperformed RoboOcc, demonstrating its robust generalization capability in complex scenarios.
Despite the significant performance improvements, VEOcc's high computational resource requirements may limit its application on resource-constrained devices. Future research can focus on optimizing its application in resource-constrained environments and enhancing its generalization capabilities in more complex scenarios.
Deep Analysis
Background
In recent years, 3D occupancy prediction has played a crucial role in autonomous exploration and scene understanding. Traditional Gaussian-centric methods have limitations in structural boundary fidelity and scene-size prior dependency, restricting their efficiency in practical applications.
Core Problem
Existing methods often perform poorly in complex scenes due to insufficient structural boundary fidelity and reliance on scene-size priors. This limits their application in dynamic and unknown environments.
Innovation
VEOcc introduces a voxel-centric representation, eliminating the need for initial scale estimation. Its Spatio-Temporal-Aware Online Update Strategy integrates Cross-Temporal Logit Aggregation, Reliability-Aware Confidence Modulation, and Confidence-Driven Incremental State Update, enhancing global semantic map consistency.
Methodology
- �� Voxel-centric framework: Eliminates the need for initial scale estimation, achieving efficient map expansion.
- �� Spatio-Temporal-Aware Online Update Strategy: Integrates Cross-Temporal Logit Aggregation, Reliability-Aware Confidence Modulation, and Confidence-Driven Incremental State Update.
- �� Experimental validation: Achieves new performance benchmarks on Occ-ScanNet and EmbodiedOcc-ScanNet.
Experiments
Experiments conducted on Occ-ScanNet and EmbodiedOcc-ScanNet, using IoU and mIoU as evaluation metrics. Compared with existing Gaussian-centric methods to verify VEOcc's performance improvements.
Results
VEOcc achieved 64.55 IoU and 55.49 mIoU on Occ-ScanNet, significantly surpassing existing Gaussian-centric baseline methods. On EmbodiedOcc-ScanNet, VEOcc also outperformed RoboOcc.
Applications
VEOcc can be applied in autonomous driving, robotic navigation, and augmented reality, providing more efficient scene understanding and navigation capabilities.
Limitations & Outlook
VEOcc's high computational resource requirements may limit its application on resource-constrained devices. Additionally, it may be affected by noise and uncertainty in complex scenes.
Plain Language Accessible to non-experts
Imagine a factory, VEOcc is like an automated production line that doesn't need pre-set product sizes. It can flexibly adjust the production process based on ever-changing demands. By integrating production data from different time points, VEOcc ensures consistency and quality of the final product. This flexibility and efficiency make VEOcc particularly effective in handling complex and unknown production environments.
ELI14 Explained like you're 14
Imagine you're playing an exploration game, VEOcc is like a super-smart map assistant. It doesn't need you to tell it how big the map is, but can automatically expand and update the map. Even if you go to a brand-new place, it can quickly adapt and give you the most accurate map information. Isn't that cool?
Glossary
Voxel-Centric
A voxel-based representation method providing uniform spatial coverage and structural boundary fidelity.
Used as the core framework for online semantic occupancy prediction.
Cross-Temporal Logit Aggregation
A strategy for enhancing temporal consistency by integrating logits from different time points.
Used in the Spatio-Temporal-Aware Online Update Strategy.
Reliability-Aware Confidence Modulation
A confidence modulation method considering spatial uncertainty.
Used to calibrate spatial uncertainty.
Confidence-Driven Incremental State Update
An incremental state update method based on confidence for global state assimilation.
Used in global semantic map assimilation.
Embodied Scene Understanding
A scene understanding method combining geometry and semantics.
Used for scene understanding supporting high-level decision-making.
Open Questions Unanswered questions from this research
- 1 How to optimize VEOcc's computational efficiency in resource-constrained environments?
- 2 How does VEOcc perform in handling extremely complex scenes?
- 3 How to further enhance VEOcc's generalization capability?
Applications
Immediate Applications
Autonomous Driving
VEOcc can be used for real-time scene understanding in autonomous vehicles, improving navigation and safety.
Long-term Vision
Smart Cities
VEOcc can be used to build dynamic map systems for smart cities, supporting urban planning and management.
Abstract
Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally constructs dense spatial representations on the fly. However, recent Gaussian-centric methods struggle with structural boundary fidelity and rely heavily on predefined scene-size priors, fundamentally limiting their operational efficiency. In this work, we present VEOcc, a voxel-centric framework formulated as a recursive perception-and-assimilation paradigm. By eliminating the need for initial scale estimation, VEOcc enables highly streamlined, open-ended map expansion. Furthermore, to robustly aggregate noisy temporal observations within the discrete voxel space, we propose a Spatio-Temporal-Aware Online Update Strategy. It integrates Cross-Temporal Logit Aggregation (TLA) for temporal consistency, Reliability-Aware Confidence Modulation (RCM) for spatial uncertainty calibration, and Confidence-Driven Incremental State Update (CSU) for robust global state assimilation. % Extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate that VEOcc establishes new state-of-the-art performance in both local and embodied settings, providing an accurate and efficient solution for real-world exploration. Extensive experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate that VEOcc establishes new state-of-the-art performance in both local and embodied settings. Notably, zero-shot evaluations on self-collected video sequences further confirm its robust out-of-distribution generalization capability in completely unseen real-world environments. Ultimately, our framework provides an accurate and highly efficient solution for autonomous exploration. Code and supplementary visualizations are available on our project page: https://wryzju.github.io/VEOcc/.