GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction (2412.04384v2)

Abstract: 3D semantic occupancy prediction is an important task for robust vision-centric autonomous driving, which predicts fine-grained geometry and semantics of the surrounding scene. Most existing methods leverage dense grid-based scene representations, overlooking the spatial sparsity of the driving scenes. Although 3D semantic Gaussian serves as an object-centric sparse alternative, most of the Gaussians still describe the empty region with low efficiency. To address this, we propose a probabilistic Gaussian superposition model which interprets each Gaussian as a probability distribution of its neighborhood being occupied and conforms to probabilistic multiplication to derive the overall geometry. Furthermore, we adopt the exact Gaussian mixture model for semantics calculation to avoid unnecessary overlapping of Gaussians. To effectively initialize Gaussians in non-empty region, we design a distribution-based initialization module which learns the pixel-aligned occupancy distribution instead of the depth of surfaces. We conduct extensive experiments on nuScenes and KITTI-360 datasets and our GaussianFormer-2 achieves state-of-the-art performance with high efficiency. Code: https://github.com/huang-yh/GaussianFormer.

• The paper introduces a probabilistic Gaussian superposition model that uses Gaussian distributions and probability multiplication to improve 3D occupancy prediction.
• It employs a distribution-based initialization to focus Gaussians on non-empty areas, reducing redundancy and enhancing computational efficiency.
• Empirical results on nuScenes and KITTI-360 show state-of-the-art per-class IoU scores and improved accuracy over traditional grid-based methods.

Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction: An Overview

The paper "Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction" presents a novel approach to improving the efficiency and effectiveness of 3D semantic occupancy predictions for autonomous driving applications. It introduces a probabilistic model that employs Gaussian distributions to model the space around objects, addressing the inefficiencies prevalent in existing grid-based and sparse representations.

Detailed Analysis of Proposed Methodology

A central contribution of the paper is the introduction of a probabilistic Gaussian superposition model. This model interprets each Gaussian as a probability distribution of its surrounding space being occupied, employing the multiplication theorem of probability to predict overall geometry, a significant departure from additive aggregation methods previously used. This probabilistic perspective not only enhances the representation's sparsity but also mitigates issues of redundancy and overlapping evident in prior models, such as GaussianFormer.

The method effectively limits the representation of Gaussians to the occupied areas in a 3D scene, thus improving computational efficiency. It also integrates a Gaussian mixture model for semantic predictions, achieving normalized output and further preventing overlap among Gaussians. This approach ensures that Gaussians are focused on non-empty regions and optimally leveraged, with geometric predictions enhanced by the probabilistic superposition framework.

Distribution-Based Initialization

The paper introduces a distribution-based initialization module to enhance the alignment of Gaussians with scene content. This module employs a data-driven methodology to initialize Gaussians around non-empty areas based on pixel-aligned occupancy distributions. It avoids reliance on additional modalities like LiDAR, which are used in some existing approaches. A noteworthy outcome of this initialization is the model's enhanced ability to adapt to the spatial distribution of objects, improving its bootstrapping from training data.

Empirical Results and Performance

The proposed GaussianFormer-2 model demonstrates superior performance on challenging datasets like nuScenes and KITTI-360, achieving state-of-the-art results with substantially fewer Gaussians than prior approaches. The empirical validation shows the model achieves high per-class Intersection-over-Union (IoU) scores, indicating improved accuracy in semantic prediction, with notable efficacy in complex scenarios involving diverse object types and environmental conditions.

Implications and Future Directions

The paper's contributions have significant implications for AI-driven environmental modeling in autonomous vehicles. By enhancing both the efficiency and accuracy of 3D occupancy predictions, the probabilistic Gaussian superposition model provides a viable pathway for implementing real-time, scalable perception systems that can operate effectively in urban environments.

Future work could explore expanding this approach to incorporate additional contextual information from multimodal sensor data, potentially enhancing robustness under varying lighting and weather conditions. Another avenue for future research could include investigating dynamic scenes, where changes over time, such as movement or deformation of objects, are integrated into the predictive framework using temporal Guassians or recurrent models.

In summary, the paper demonstrates a compelling advancement in 3D scene representation technology, providing a valuable tool for researchers and practitioners aiming to enhance the situational awareness of autonomous systems. The probabilistic perspective on Gaussian representation offers a new lens through which the efficiency and scalability of 3D perception models can be significantly improved.