Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction
This work addresses the need for efficient 3D scene understanding in applications like robotics and autonomous driving, though it appears incremental as it builds on existing SSC methods.
The paper tackled the problem of real-time 3D semantic scene completion by proposing a method with feature aggregation and conditioned prediction, achieving competitive performance at 110 FPS on benchmarks like NYU, NYUCAD, and SUNCG.
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.