Sparsity-Aware Voxel Attention and Foreground Modulation for 3D Semantic Scene Completion

TL;DR

This paper introduces VoxSAMNet, a novel framework for monocular 3D semantic scene completion that explicitly models voxel sparsity and semantic imbalance, achieving state-of-the-art results.

Contribution

VoxSAMNet employs a dummy shortcut for feature refinement and a foreground modulation strategy to improve accuracy and generalization in sparse 3D scene completion.

Findings

Achieves 18.2% mIoU on SemanticKITTI benchmark.

Outperforms prior monocular and stereo methods.

Demonstrates the effectiveness of sparsity-aware and semantics-guided design.

Abstract

Monocular Semantic Scene Completion (SSC) aims to reconstruct complete 3D semantic scenes from a single RGB image, offering a cost-effective solution for autonomous driving and robotics. However, the inherently imbalanced nature of voxel distributions, where over 93% of voxels are empty and foreground classes are rare, poses significant challenges. Existing methods often suffer from redundant emphasis on uninformative voxels and poor generalization to long-tailed categories. To address these issues, we propose VoxSAMNet (Voxel Sparsity-Aware Modulation Network), a unified framework that explicitly models voxel sparsity and semantic imbalance. Our approach introduces: (1) a Dummy Shortcut for Feature Refinement (DSFR) module that bypasses empty voxels via a shared dummy node while refining occupied ones with deformable attention; and (2) a Foreground Modulation Strategy combining Foreground Dropout (FD) and Text-Guided Image Filter (TGIF) to alleviate overfitting and enhance class-relevant features. Extensive experiments on the public benchmarks SemanticKITTI and SSCBench-KITTI-360 demonstrate that VoxSAMNet achieves state-of-the-art performance, surpassing prior monocular and stereo baselines with mIoU scores of 18.2% and 20.2%, respectively. Our results highlight the importance of sparsity-aware and semantics-guided design for efficient and accurate 3D scene completion, offering a promising direction for future research.