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

arXiv: https://arxiv.org/abs/2311.17084 · HTML: https://mlsanity.com/paper/2311.17084/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://mlsanity.com/paper/2112.11790/) (2021)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2302.07817/) (2023)
- [MonoScene: Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2112.00726/) (2021)
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://mlsanity.com/paper/2304.14365/) (2023)
- [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2303.09551/) (2023)
- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.12251/) (2023)
- [OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2304.05316/) (2023)
- [Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion](https://mlsanity.com/paper/2012.03762/) (2020)
- [Non-Local Spatial Propagation Network for Depth Completion](https://mlsanity.com/paper/2007.10042/) (2020)
- [LMSCNet: Lightweight Multiscale 3D Semantic Completion](https://mlsanity.com/paper/2008.10559/) (2020)
- [Anisotropic Convolutional Networks for 3D Semantic Scene Completion](https://mlsanity.com/paper/2004.02122/) (2020)
- [S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds](https://mlsanity.com/paper/2012.09242/) (2020)
- [Symphonize 3D Semantic Scene Completion with Contextual Instance Queries](https://mlsanity.com/paper/2306.15670/) (2023)
- [NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space](https://mlsanity.com/paper/2309.14616/) (2023)
- [OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.13540/) (2023)
- [SCPNet: Semantic Scene Completion on Point Cloud](https://mlsanity.com/paper/2303.06884/) (2023)
- [SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving](https://mlsanity.com/paper/2306.09001/) (2023)
- [Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data](https://mlsanity.com/paper/2011.09141/) (2020)
- [Fast and Robust Iterative Closest Point](https://mlsanity.com/paper/2007.07627/) (2020)
- [Efficient Semantic Scene Completion Network with Spatial Group Convolution](https://mlsanity.com/paper/1907.05091/) (2019)
- [RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion](https://mlsanity.com/paper/1903.00620/) (2019)
- [Two Stream 3D Semantic Scene Completion](https://mlsanity.com/paper/1804.03550/) (2018)
- [Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion](https://mlsanity.com/paper/1908.00382/) (2019)

## Built on by (most cited first, 30 of 32)

- [Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views](https://mlsanity.com/paper/2510.18632/) (2025)
- [Camera-based 3D Semantic Scene Completion with Sparse Guidance Network](https://mlsanity.com/paper/2312.05752/) (2023)
- [A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective](https://mlsanity.com/paper/2405.05173/) (2024)
- [DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Driving](https://mlsanity.com/paper/2412.09043/) (2024)
- [MonoFusion: Sparse-View 4D Reconstruction via Monocular Fusion](https://mlsanity.com/paper/2507.23782/) (2025)
- [Top-Down Semantic Refinement for Image Captioning](https://mlsanity.com/paper/2510.22391/) (2025)
- [Building Lane-Level Maps from Aerial Images](https://mlsanity.com/paper/2312.13449/) (2023)
- [Offboard Occupancy Refinement with Hybrid Propagation for Autonomous Driving](https://mlsanity.com/paper/2403.08504/) (2024)
- [FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models](https://mlsanity.com/paper/2512.20561/) (2025)
- [Does Bigger Mean Better? Comparitive Analysis of CNNs and Biomedical Vision Language Modles in Medical Diagnosis](https://mlsanity.com/paper/2510.00411/) (2025)
- [STORM: Search-Guided Generative World Models for Robotic Manipulation](https://mlsanity.com/paper/2512.18477/) (2025)
- [Rainbow Noise: Stress-Testing Multimodal Harmful-Meme Detectors on LGBTQ Content](https://mlsanity.com/paper/2507.19551/) (2025)
- [DAP: Domain-aware Prompt Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2311.17812/) (2023)
- [CoAgent: Collaborative Planning and Consistency Agent for Coherent Video Generation](https://mlsanity.com/paper/2512.22536/) (2025)
- [Enhancing 3D Semantic Scene Completion with a Refinement Module](https://mlsanity.com/paper/2512.18363/) (2025)
- [FlowSSC: Universal Generative Monocular Semantic Scene Completion via One-Step Latent Diffusion](https://mlsanity.com/paper/2601.15250/) (2026)
- [AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents](https://mlsanity.com/paper/2602.15325/) (2026)
- [MoE-DiffuSeq: Enhancing Long-Document Diffusion Models with Sparse Attention and Mixture of Experts](https://mlsanity.com/paper/2512.20604/) (2025)
- [HD2-SSC: High-Dimension High-Density Semantic Scene Completion for Autonomous Driving](https://mlsanity.com/paper/2511.07925/) (2025)
- [A Three-Level Alignment Framework for Large-Scale 3D Retrieval and Controlled 4D Generation](https://mlsanity.com/paper/2512.22294/) (2025)
- [Learning Dynamic Scene Reconstruction with Sinusoidal Geometric Priors](https://mlsanity.com/paper/2512.22295/) (2025)
- [Towards Efficient Post-Training via Fourier-Driven Adapter Architectures](https://mlsanity.com/paper/2512.22378/) (2025)
- [Self-Rewarded Multimodal Coherent Reasoning Across Diverse Visual Domains](https://mlsanity.com/paper/2512.22545/) (2025)
- [Multi-Agent Cooperative Learning for Robust Vision-Language Alignment under OOD Concepts](https://mlsanity.com/paper/2601.09746/) (2026)
- [Bridging the Semantic Chasm: Synergistic Conceptual Anchoring for Generalized Few-Shot and Zero-Shot OOD Perception](https://mlsanity.com/paper/2602.00340/) (2026)
- [Multi-Resolution Alignment for Voxel Sparsity in Camera-Based 3D Semantic Scene Completion](https://mlsanity.com/paper/2602.03371/) (2026)
- [A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining](https://mlsanity.com/paper/2602.15330/) (2026)
- [CloudDiffusion: Diffusion-Based Scene Completion in the Point Cloud Domain](https://mlsanity.com/paper/2606.16048/) (2026)
- [RayLift: Lifting Complementary Ray-Wise Evidence with 3D Geometry Priors for Semantic Scene Completion](https://mlsanity.com/paper/2608.08476/) (2026)
- [SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion](https://mlsanity.com/paper/2609.17413/) (2026)

## Abstract (arXiv)

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.