NEWS ARTICLE
学术动态：Distillation Style Regulators and Semantic Prior-Guided Framework for Non-Ideal Single-View 3D Vehicle Point Cloud.
论文标题：Distillation Style Regulators and Semantic Prior-Guided Framework for Non-Ideal Single-View 3D Vehicle Point Cloud Reconstruction
发布日期：2026-05-26
作者：Jinghao Cao, Xiajun Liu, Rui Xue
DOI：10.3390/s26113359
论文摘要：The closed-loop testing of autonomous driving systems critically depends on large-scale libraries of diverse and realistic 3D vehicle assets, yet current pipelines still rely on labor-intensive modeling or multi-view capture, making efficient construction a key bottleneck. To overcome this bottleneck and enable convenient, cost-effective 3D asset generation, we propose a semantic prior-guided framework for accurate and robust vehicle point cloud reconstruction from casually captured single-view photographs. Our framework is built on a diffusion backbone but is fundamentally driven by two forms of prior knowledge: First, geometric and appearance priors from camera-aware image features, masks, and distance-transform maps are projected onto the evolving point cloud, compensating for the severe information loss in single-view inputs. Second, we introduce distillation-style regulators—pretrained neural networks that encode vehicle type and model semantics; they act as teacher networks that impose high-level constraints on the generated point clouds, transferring rich semantic knowledge and effectively regularizing the learning process. With these priors, our model infers vehicle-specific semantics from limited observations and reconstructs high-quality 3D point cloud assets. On the 3DRealCar++ dataset, our method clearly surpasses state-of-the-art point cloud baselines in both F-score and Chamfer Distance.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113359. Vol. 26, Issue 11. Authors: Jinghao Cao, Xiajun Liu, Rui Xue.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/s26113359
PDF 链接：https://www.mdpi.com/1424-8220/26/11/3359/pdf