Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction

• URL: http://arxiv.org/abs/2303.10967v2
• Date: Sat, 25 Mar 2023 07:36:34 GMT
• Title: Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction
• Authors: Xiaokang Chen, Yajie Xing and Gang Zeng
• Abstract summary: We propose a real-time semantic scene completion method with a feature aggregation strategy and conditioned prediction module. Our method achieves competitive performance at a speed of 110 FPS on one GTX 1080 Ti GPU.
• Score: 17.54862035445157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. In this paper, we propose a real-time semantic scene completion method with a feature aggregation strategy and conditioned prediction module. Feature aggregation fuses feature with different receptive fields and gathers context to improve scene completion performance. And the conditioned prediction module adopts a two-step prediction scheme that takes volumetric occupancy as a condition to enhance semantic completion prediction. We conduct experiments on three recognized benchmarks NYU, NYUCAD, and SUNCG. Our method achieves competitive performance at a speed of 110 FPS on one GTX 1080 Ti GPU.

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