Abstract
Camera-based 3D Semantic Scene Completion (SSC) is a critical task for autonomous driving and robotic scene understanding. It aims to infer a complete 3D volumetric representation of both semantics and geometry from a single image. Existing methods typically focus on end-to-end 2D-to-3D feature lifting and voxel completion. However, they often overlook the interference between high-confidence visible-region perception and low-confidence occluded-region reasoning caused by single-image input, which can lead to feature dilution and error propagation. To address these challenges, we introduce an offline Visible Region Label Extraction (VRLE) strategy that explicitly separates and extracts voxel-level supervision for visible regions from dense 3D ground truth. This strategy purifies the supervisory space for two complementary sub-tasks: visible-region perception and occluded-region reasoning. Building on this idea, we propose the Visible-Occluded Interactive Completion Network (VOIC), a novel dual-decoder framework that explicitly decouples SSC into visible-region semantic perception and occluded-region scene completion. VOIC first constructs a base 3D voxel representation by fusing image features with depth-derived occupancy. The visible decoder focuses on generating high-fidelity geometric and semantic priors, while the occlusion decoder leverages these priors together with cross-modal interaction to perform coherent global scene reasoning. Extensive experiments on the SemanticKITTI and SSCBench-KITTI360 benchmarks demonstrate that VOIC outperforms existing monocular SSC methods in both geometric completion and semantic segmentation accuracy, achieving state-of-the-art performance.
Abstract (translated)
基于相机的三维语义场景完成（SSC）是自主驾驶和机器人场景理解中的关键任务，其目标是从单张图像中推断出完整的3D体积表示，涵盖几何和语义信息。现有的方法通常集中于端到端的2D至3D特征提升及体素补全。然而，这些方法往往忽视了由单一图像输入所导致的高置信度可视区域感知与低置信度被遮挡区域推理之间的干扰问题，这些问题可能导致特征稀释和误差传播。 为了解决这些挑战，我们提出了一种离线可见区域标签提取（VRLE）策略，该策略明确地将密集3D真实标签中的体素级监督分离并抽取给可视区域。这一策略通过精炼两种互补子任务的指导空间来提高准确性和鲁棒性：可视区域感知和遮挡区域推理。 在此基础上，我们提出了可见-遮挡交互完成网络（VOIC），这是一种新颖的双解码器框架，它明确地将SSC拆分为两个部分：可视区域语义感知与被遮挡区域场景补全。VOIC首先通过融合图像特征与深度衍生的占用信息来构建基础3D体素表示。可见区域解码器专注于生成高保真度的几何和语义先验，而遮挡区域解码器则利用这些先验及跨模态交互进行一致的整体场景推理。 在SemanticKITTI和SSCBench-KITTI360基准测试中的广泛实验表明，VOIC优于现有的单目SSC方法，在几何补全和语义分割精度方面均达到了最先进的性能。
URL
https://arxiv.org/abs/2512.18954
