Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction (2302.07817v2)

Abstract: Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://github.com/wzzheng/TPVFormer.

• The paper introduces a novel Tri-Perspective View (TPV) framework that integrates three orthogonal planes to overcome BEV limitations.
• The TPVFormer encoder employs deformable attention mechanisms to efficiently process multi-view features from RGB inputs, yielding high-resolution 3D occupancy predictions.
• Experimental results demonstrate that TPV rivals LiDAR-based methods on benchmarks like nuScenes and SemanticKITTI, highlighting its potential for cost-effective autonomous perception.

Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction

The paper "Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction" addresses the complexities inherent in autonomous driving perception by proposing a novel representation approach termed the Tri-Perspective View (TPV). This method seeks to surmount the limitations observed in traditional Bird’s-Eye-View (BEV) representations by integrating two additional orthogonal viewpoints, thus enhancing the model's ability to discern fine-grained 3D structures.

Methodology and Contributions

The core innovation of the paper is the TPV representation, which complements BEV by adding side and front planes. This tri-plane approach aims to encapsulate comprehensive spatial features without the computational burden typically associated with dense voxel representations. The authors leverage a transformer-based encoder, TPVFormer, to effectively process these tri-perspective features from RGB camera inputs using attention mechanisms.

Key points of the methodology include:

• TPV Representation: The TPV framework utilizes three orthogonal planes, allowing for a more exhaustive capture of spatial information across multiple dimensions, reducing information loss from flat projections inherent in BEV methods.
• TPVFormer Encoder: This model employs deformable attention techniques to assimilate feature information from 2D images, efficiently projecting it into the TPV space. This facilitates high-resolution predictions while maintaining efficient computation.
• Occupancy Prediction and Semantic Segmentation: The work demonstrates that TPVFormer, trained with sparse LiDAR-derived labels, can predict dense 3D semantic occupancy comparable to or better than methods relying on LiDAR data, particularly in the absence of direct depth inputs.

Results and Implications

The experimental outcomes outlined in the paper validate the efficacy of TPVFormer across several benchmarks:

• LiDAR Segmentation: Despite using only image inputs, TPVFormer achieves comparable performance to LiDAR-based approaches on the nuScenes dataset, suggesting potential for reducing reliance on expensive LiDAR systems in autonomous driving.
• Semantic Scene Completion: The TPV method exhibits superior performance against existing vision-based models like MonoScene, particularly in capturing semantic classes in outdoor settings as tested on the SemanticKITTI dataset.
• Resolution Flexibility: The architecture allows modification of output resolution at inference time, enhancing its usability in scenarios requiring varying degrees of detail without necessitating retraining.

These contributions advance the understanding of how multi-view integration can enhance 3D perception in autonomous systems. The TPV approach, through its efficient representation, provides a pathway for vision-centric systems to approach the robustness and accuracy traditionally afforded by LiDAR.

Future Directions

This work opens several avenues for further exploration. The TPV framework could benefit from integration with additional sensory modalities to further increase the fidelity of spatial reconstruction. Additionally, exploring its application in dynamic scene understanding could augment its utility in real-time perception systems. Future endeavors could also consider optimizing transformer model efficiency or extending TPV to other domains requiring complex spatial reasoning, such as robotics or augmented reality.

Overall, the tri-perspective approach offers a robust framework for enhancing vision-based 3D perception, bridging the gap between comprehensive environmental understanding and computational feasibility.