PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction

• URL: http://arxiv.org/abs/2412.07616v2
• Date: Wed, 18 Dec 2024 10:02:35 GMT
• Title: PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction
• Authors: Yujing Xue, Jiaxiang Liu, Jiawei Du, Joey Tianyi Zhou,
• Abstract summary: We introduce the Polar Voxel Occupancy Predictor (PVP), a novel 3D multi-modal predictor that operates in polar coordinates. PVP features two key design elements to overcome distortion: a Global Represent Propagation module that integrates global spatial data into 3D volumes, and a Plane Devolution Concomposed (PD-Conv) that simplifies 3D distortions into 2D convolutions. These innovations enable PVP to outperform existing methods, achieving significant improvements in mIoU and IoU metrics on the OpenOccupancy dataset.
• Score: 38.426636518614096
• License:
• Abstract: Recently, polar coordinate-based representations have shown promise for 3D perceptual tasks. Compared to Cartesian methods, polar grids provide a viable alternative, offering better detail preservation in nearby spaces while covering larger areas. However, they face feature distortion due to non-uniform division. To address these issues, we introduce the Polar Voxel Occupancy Predictor (PVP), a novel 3D multi-modal predictor that operates in polar coordinates. PVP features two key design elements to overcome distortion: a Global Represent Propagation (GRP) module that integrates global spatial data into 3D volumes, and a Plane Decomposed Convolution (PD-Conv) that simplifies 3D distortions into 2D convolutions. These innovations enable PVP to outperform existing methods, achieving significant improvements in mIoU and IoU metrics on the OpenOccupancy dataset.

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