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DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation

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arxiv 2311.17084 v2 pith:TVWEOZJP submitted 2023-11-28 cs.CV

Jiawei Yao , Jusheng Zhang , Xiaochao Pan , Tong Wu , Canran Xiao This is my paper
classification cs.CV
keywords depthsscscenesemanticcompletionmonocularobjectvoxelalignment
verification ladder T0 review T1 audit T2 compute T3 formal
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The task of 3D semantic scene completion using monocular cameras is gaining significant attention in the field of autonomous driving. This task aims to predict the occupancy status and semantic labels of each voxel in a 3D scene from partial image inputs. Despite numerous existing methods, many face challenges such as inaccurately predicting object shapes and misclassifying object boundaries. To address these issues, we propose DepthSSC, an advanced method for semantic scene completion using only monocular cameras. DepthSSC integrates the Spatial Transformation Graph Fusion (ST-GF) module with Geometric-Aware Voxelization (GAV), enabling dynamic adjustment of voxel resolution to accommodate the geometric complexity of 3D space. This ensures precise alignment between spatial and depth information, effectively mitigating issues such as object boundary distortion and incorrect depth perception found in previous methods. Evaluations on the SemanticKITTI and SSCBench-KITTI-360 dataset demonstrate that DepthSSC not only captures intricate 3D structural details effectively but also achieves state-of-the-art performance.

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Cited by 3 Pith papers

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  1. One Step Closer: Creating the Future to Boost Monocular Semantic Scene Completion

    cs.CV 2025-07 conditional novelty 7.0 of 10

    CF-SSC predicts pseudo-future frames from past and current monocular images and fuses them in 3D, achieving state-of-the-art semantic scene completion on SemanticKITTI and SSCBench-KITTI-360.

  2. Disentangling Instance and Scene Contexts for 3D Semantic Scene Completion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dual-stream BEV architecture that separates instance and scene class queries achieves state-of-the-art mIoU of 17.35 on SemanticKITTI and 20.55 on SSCBench-KITTI-360.

  3. VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection

    cs.GR 2025-06 conditional novelty 6.0 of 10

    VoxDet reformulates 3D semantic occupancy prediction as dense object detection by deriving instance-boundary offsets from voxel class labels, and reports new state-of-the-art results on camera and LiDAR benchmarks.

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