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Camera-based 3d Semantic Scene Completion With Sparse Guidance Network.

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Camera-based 3d Semantic Scene Completion With Sparse Guidance Network.

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|September 26, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces SGN, a novel one-stage framework for camera-based semantic scene completion (SSC) in autonomous driving. SGN effectively propagates semantic information using spatial geometry cues, achieving superior performance and efficiency.

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Semantic Scene Completion (SSC) is crucial for autonomous driving, predicting 3D scene occupancy from limited observations.
Camera-based SSC methods offer advantages but struggle with discriminative segmentation due to heavy 3D models.
Existing approaches lack sufficient feature separation and efficient semantic propagation.

Purpose of the Study:

To propose SGN, a one-stage, camera-based SSC framework using a dense-sparse-dense design.
To enhance semantic propagation using spatial geometry cues and a novel sparse voxel proposal network.
To improve feature separation and accelerate convergence for accurate 3D scene understanding.

Main Methods:

Redesigned sparse voxel proposal network for depth-aware context and coarse-to-fine processing.

Implemented hybrid guidance (sparse semantic and geometry) and effective voxel aggregation.

Developed a multi-scale semantic propagation module for flexible receptive fields and reduced computation.

Main Results:

SGN demonstrates superior performance on SemanticKITTI and SSCBench-KITTI-360 datasets.
The lightweight SGN-L achieves 14.80% mIoU and 45.45% IoU on SemanticKITTI validation.
SGN-L utilizes only 12.5M parameters and 7.16G training memory, showcasing efficiency.

Conclusions:

SGN offers a highly effective and efficient solution for camera-based semantic scene completion.
The proposed methods significantly improve feature separation and semantic propagation accuracy.
SGN provides a strong baseline for future research in autonomous driving perception.