VOIC: Visible-Occluded Decoupling for Monocular 3D Semantic Scene Completion

• URL: http://arxiv.org/abs/2512.18954v1
• Date: Mon, 22 Dec 2025 02:05:45 GMT
• Title: VOIC: Visible-Occluded Decoupling for Monocular 3D Semantic Scene Completion
• Abstract summary: Camera-based 3D Semantic Scene Completion is a critical task for autonomous driving and robotic scene understanding.<n>Existing methods typically focus on end-to-end 2D-to-3D feature lifting and voxel completion.<n>We propose a novel dual-decoder framework that explicitly decouples SSC into visible-region semantic perception and occluded-region scene completion.
• Score: 6.144392125326462
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction [63.738631229597274]
  Vision-based 3D occupancy prediction fundamentally relies on the 2D-to-3D view transformation.<n>Current paradigms predominantly utilize explicit physical projection, which artificially restricts the routing matrix to strict, sparse camera rays.<n>We propose Factorized Dense Routing (FDR) to break this bottleneck.<n>Our framework achieves state-the-art performance on the Occ3D-nuScenes and Occ3D-Waymo benchmarks.
  arXiv  Detail & Related papers  (2026-07-04T11:12:42Z)
• SpatialFly: Geometry-Guided Representation Alignment for UAV Vision-and-Language Navigation in Urban Environments [49.966170814478915]
  We propose a geometry-guided spatial representation framework for UAV VLN.<n>Running on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D representation alignment mechanism.<n> Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments.
  arXiv  Detail & Related papers  (2026-03-22T03:56:58Z)
• PC-CrossDiff: Point-Cluster Dual-Level Cross-Modal Differential Attention for Unified 3D Referring and Segmentation [40.5034963034718]
  3D Visual Grounding aims to localize the referent of natural language referring expressions through two core tasks: Referring Expression (3DREC) and (3DRES)<n>Existing methods face two key challenges in complex, multi-object scenes: inadequate parsing of implicit localization cues and ineffective suppression of dynamic spatial interference from co-occurring objects.<n>We propose PC-CrossDiff, a unified dual-task framework with a dual-level cross-modal differential attention architecture for 3DREC and 3DRES.
  arXiv  Detail & Related papers  (2026-03-18T14:16:48Z)
• OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder [90.8453349494245]
  We present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space.<n>OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods.
  arXiv  Detail & Related papers  (2026-03-17T03:43:37Z)
• Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing [59.12032628787018]
  3D mesh saliency ground truth is essential for human-centric visual modeling in virtual reality (VR)<n>Current VR eye-tracking pipelines rely on single ray sampling and Euclidean smoothing, triggering texture attention and signal leakage across gaps.<n>This paper proposes a robust framework to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T05:20:12Z)
• HD$^2$-SSC: High-Dimension High-Density Semantic Scene Completion for Autonomous Driving [52.959716866316604]
  Camera-based 3D semantic scene completion (SSC) plays a crucial role in autonomous driving.<n>Existing SSC methods suffer from the inherent input-output dimension gap and annotation-reality density gap.<n>We propose a corresponding High- Dimension High-Density Semantic Scene Completion framework with expanded pixel semantics and refined voxel occupancies.
  arXiv  Detail & Related papers  (2025-11-11T07:24:35Z)
• SPHERE: Semantic-PHysical Engaged REpresentation for 3D Semantic Scene Completion [52.959716866316604]
  Camera-based 3D Semantic Scene Completion (SSC) is a critical task in autonomous driving systems.<n>We propose the Semantic-PHysical Engaged REpresentation (SPHERE) for camera-based SSC.<n>SPHERE integrates voxel and Gaussian representations for joint exploitation of semantic and physical information.
  arXiv  Detail & Related papers  (2025-09-14T09:07:41Z)
• VisHall3D: Monocular Semantic Scene Completion from Reconstructing the Visible Regions to Hallucinating the Invisible Regions [8.379218197127216]
  VisHall3D decomposes the scene completion task into two stages: reconstructing the visible regions (vision) and inferring the invisible regions (hallucination)<n> VisHall3D achieves state-of-the-art performance, outperforming previous methods by a significant margin.
  arXiv  Detail & Related papers  (2025-07-25T11:57:18Z)
• Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation [54.04601077224252]
  Embodied scene understanding requires not only comprehending visual-spatial information but also determining where to explore next in the 3D physical world.<n>underlinetextbf3D vision-language learning enables embodied agents to effectively explore and understand their environment.<n>model's versatility enables navigation using diverse input modalities, including categories, language descriptions, and reference images.
  arXiv  Detail & Related papers  (2025-07-05T14:15:52Z)
• CA-W3D: Leveraging Context-Aware Knowledge for Weakly Supervised Monocular 3D Detection [5.881158575425763]
  We propose a Context-Aware Weak Supervision for Monocular 3D object detection, namely CA-W3D, to address this limitation in a two-stage training paradigm.<n>Specifically, we first introduce a pre-training stage employing Region-wise Object Contrastive Matching (ROCM), which aligns regional object embeddings derived from a trainable monocular 3D encoder and a frozen open-vocabulary 2D visual grounding model.<n>In the second stage, we incorporate a pseudo-label training process with a Dual-to-One Distillation (D2OD) mechanism, which effectively transfers contextual priors into
  arXiv  Detail & Related papers  (2025-03-06T07:02:13Z)
• Grounding 3D Scene Affordance From Egocentric Interactions [52.5827242925951]
  Grounding 3D scene affordance aims to locate interactive regions in 3D environments. We introduce a novel task: grounding 3D scene affordance from egocentric interactions.
  arXiv  Detail & Related papers  (2024-09-29T10:46:19Z)
• DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation [2.949710700293865]
  We propose DepthSSC, an advanced method for semantic scene completion using only monocular cameras.<n> DepthSSC integrates the Spatial Transformation Graph Fusion (ST-GF) module with Geometric-Aware Voxelization (GAV)<n>We show that DepthSSC captures intricate 3D structural details effectively and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-11-28T01:47:51Z)
• Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion [45.171150395915056]
  3D semantic scene completion (SSC) is an ill-posed perception task that requires inferring a dense 3D scene from limited observations. Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations. We resort to stereo matching technique and bird's-eye-view (BEV) representation learning to address such issues in SSC.
  arXiv  Detail & Related papers  (2023-03-24T12:33:44Z)
• Semantic Scene Completion with Cleaner Self [93.99441599791275]
  Semantic Scene Completion (SSC) transforms an image of single-view depth and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a well-known ill-posed problem as the prediction model has to "imagine" what is behind the visible surface, which is usually represented by Truncated Signed Distance Function (TSDF) We use the ground-truth 3D voxels to generate a perfect visible surface, called TSDF-CAD, and then train a "cleaner" SSC model. As the model is noise-free, it is expected to
  arXiv  Detail & Related papers  (2023-03-17T13:50:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.