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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Temporal and spatial context aware voxel transformer for semantic scene completion.

Yiqi Wu1, Changliang Li2, Jiale He2

  • 1School of Computer Science, China University of Geosciences, Wuhan, Hubei, 430078, China; Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), Wuhan, Hubei, 430205, China.

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Summary
This summary is machine-generated.

This study introduces a new method for Semantic Scene Completion (SSC) in autonomous driving, improving 3D understanding by integrating multi-frame context and depth cues for more accurate perception.

Keywords:
3D semantic reconstructionDepth estimation and fusionSemantic scene completionTemporal-spatial reasoning

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Semantic Scene Completion (SSC) is vital for autonomous driving perception.
  • Existing camera-based SSC methods struggle with temporal reasoning and depth estimation, leading to incomplete geometry and semantics.

Purpose of the Study:

  • To develop an advanced method for complete 3D semantic scene recovery.
  • To enhance temporal reasoning and depth estimation accuracy in SSC.

Main Methods:

  • Progressively integrating multi-frame context and depth cues for 3D semantic recovery.
  • Temporally aligning contextual features using temporal-spatial aware mechanisms.
  • Probabilistically fusing monocular depth priors with stereo constraints for refined depth estimation.

Main Results:

  • The proposed method achieves accurate and stable semantic scene completion.
  • Evaluated on SemanticKITTI and SSCBench-KITTI-360, outperforming state-of-the-art methods.

Conclusions:

  • The novel approach effectively addresses limitations in current SSC methods.
  • Improved temporal reasoning and depth fusion lead to superior 3D semantic understanding for autonomous systems.