Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving

TL;DR

OccFiner is an offboard framework that significantly improves vision-based 3D occupancy prediction accuracy in autonomous driving by using hybrid local and global propagation techniques, setting new state-of-the-art results.

Contribution

The paper introduces OccFiner, a novel offboard occupancy refinement framework that combines local and global propagation to enhance accuracy and enable automatic annotation in vision-based SSC.

Findings

Achieves state-of-the-art accuracy on SemanticKITTI dataset.

Significantly improves geometric and semantic occupancy predictions.

Facilitates occupancy data loop-closure in autonomous driving.

Abstract

Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous geometric and semantic estimation, continuity across varying viewpoints, and single-view occlusion. Our paper introduces OccFiner, a novel offboard framework designed to enhance the accuracy of vision-based occupancy predictions. OccFiner operates in two hybrid phases: 1) a multi-to-multi local propagation network that implicitly aligns and processes multiple local frames for correcting onboard model errors and consistently enhancing occupancy accuracy across all distances. 2) the region-centric global propagation, focuses on refining labels using explicit multi-view geometry and integrating sensor bias, particularly for increasing the accuracy of distant occupied voxels. Extensive experiments demonstrate that OccFiner improves both geometric and semantic accuracy across various types of coarse occupancy, setting a new state-of-the-art performance on the SemanticKITTI dataset. Notably, OccFiner significantly boosts the performance of vision-based SSC models, achieving accuracy levels competitive with established LiDAR-based onboard SSC methods. Furthermore, OccFiner is the first to achieve automatic annotation of SSC in a purely vision-based approach. Quantitative experiments prove that OccFiner successfully facilitates occupancy data loop-closure in autonomous driving. Additionally, we quantitatively and qualitatively validate the superiority of the offboard approach on city-level SSC static maps. The source code will be made publicly available at https://github.com/MasterHow/OccFiner.