Panoptic-FlashOcc: An Efficient Baseline to Marry Semantic Occupancy with Panoptic via Instance Center (2406.10527v2)

Abstract: Panoptic occupancy poses a novel challenge by aiming to integrate instance occupancy and semantic occupancy within a unified framework. However, there is still a lack of efficient solutions for panoptic occupancy. In this paper, we propose Panoptic-FlashOcc, a straightforward yet robust 2D feature framework that enables realtime panoptic occupancy. Building upon the lightweight design of FlashOcc, our approach simultaneously learns semantic occupancy and class-aware instance clustering in a single network, these outputs are jointly incorporated through panoptic occupancy procession for panoptic occupancy. This approach effectively addresses the drawbacks of high memory and computation requirements associated with three-dimensional voxel-level representations. With its straightforward and efficient design that facilitates easy deployment, Panoptic-FlashOcc demonstrates remarkable achievements in panoptic occupancy prediction. On the Occ3D-nuScenes benchmark, it achieves exceptional performance, with 38.5 RayIoU and 29.1 mIoU for semantic occupancy, operating at a rapid speed of 43.9 FPS. Furthermore, it attains a notable score of 16.0 RayPQ for panoptic occupancy, accompanied by a fast inference speed of 30.2 FPS. These results surpass the performance of existing methodologies in terms of both speed and accuracy. The source code and trained models can be found at the following github repository: https://github.com/Yzichen/FlashOCC.

• The paper introduces Panoptic-FlashOcc, a framework that unifies semantic occupancy and instance clustering through efficient 2D convolutions, bypassing expensive 3D operations.
• Its streamlined architecture leverages a centerness head and channel-to-height transformation, attaining 38.5 RayIoU and 29.1 mIoU at real-time speeds on Occ3D-nuScenes.
• Ablation studies confirm that integrating semantic and geometric affinity losses significantly boosts performance, underscoring its practical applicability in autonomous navigation.

Panoptic-FlashOcc: An Efficient Baseline for Integrated Semantic and Instance Occupancy Prediction

The paper "Panoptic-FlashOcc: An Efficient Baseline to Marry Semantic Occupancy with Panoptic via Instance Center" presents an innovative solution to the complex challenge of panoptic occupancy in 3D scene understanding. The authors introduce Panoptic-FlashOcc, a framework that effectively integrates semantic occupancy and instance-level categorization within a streamlined two-dimensional feature network. By leveraging the architectural simplicity and performance advantages of FlashOcc, this work offers substantial improvements in both speed and accuracy over existing methods.

Core Contributions and Methodology

Panoptic occupancy is a pivotal component in automotive and robotic applications, demanding high accuracy and real-time inference capability. The proposed Panoptic-FlashOcc framework addresses these demands through:

1. Framework Design: The approach integrates semantic occupancy predictions and class-aware instance clustering into a unified network. This design bypasses the computational costs associated with traditional 3D voxel-level approaches, making deployment on edge devices more feasible.
2. Architectural Efficiency: Panoptic-FlashOcc employs a simplified architecture that incorporates a centerness head inspired by Panoptic-DeepLab, combined with a channel-to-height transformation from FlashOcc. This configuration enables efficient conversion of flattened BEV features into 3D occupancy predictions using only 2D convolutions, eliminating the need for expensive 3D operations.
3. Panoptic Processing: The paper introduces a pragmatic post-processing step for assigning instance IDs, which involves non-complex operations like matrix manipulation and logical assessment. This method effectively generates panoptic occupancy without burdening the network with additional trainable parameters.

Experimental Validation and Results

The paper rigorously evaluates the proposed method on the Occ3D-nuScenes benchmark. Key findings include:

• Performance Metrics: Panoptic-FlashOcc achieves 38.5 RayIoU and 29.1 mIoU for semantic occupancy at a speed of 43.9 FPS, and 16.0 RayPQ for panoptic occupancy at 30.2 FPS. These results convincingly outperform existing methods in both accuracy and inference speed.
• Comparison with Baselines: The proposed method consistently outperforms SparseOcc, a current benchmark in the field, demonstrating substantial gains in RayIoU and maintaining superior frame rates across different experimental setups.
• Ablation Studies: The paper presents ablation studies to verify the contributions of various components, demonstrating that semantic and geometric affinity losses markedly boost performance, underscoring the module’s efficacy.

Theoretical and Practical Implications

The concise and efficient construction of Panoptic-FlashOcc establishes a compelling alternative for real-time panoptic occupancy prediction, essential for diverse applications like autonomous navigation and environmental mapping. The avoidance of 3D convolutions not only enhances inference speeds but also broadens deployment scenarios to include a range of hardware environments beyond those optimized for 3D operations.

Future Prospects

While this research mainly targets urban scenes, the authors acknowledge potential challenges when adapting panoptic occupancy to indoor environments due to object overlap. Future work may focus on extending the architecture with additional layers to address height-variant occupancy, improving its versatility across applications.

In conclusion, Panoptic-FlashOcc stands as a substantial contribution to the domain of 3D scene understanding, offering an impactful blend of methodological innovation and practical deployment. It sets a new standard in integrated semantic and instance occupancy prediction, paving the way for future advancements in both research and application within the field.