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<p>Three-dimensional point cloud reconstruction aims to recover continuous, geometrically consistent surfaces from sparse, unstructured samples, playing a pivotal role in 3D perception and modeling systems. Traditional methods often struggle with spatial nonuniformity and abrupt geometric changes, leading to over-smoothing or artifacts. To address these challenges, we introduce a geometry-complexity-aware diffusion (GCAD) framework that employs diffusion as a region-adaptive feature refiner. This framework utilizes regional complexity scores to drive adaptive timestep allocation and integrates multiscale features via a confidence-aware fusion module, improving stability and consistency under sparse and noisy inputs. Experiments on ShapeNet, SyntheticRoom, and ScanNet demonstrate consistent improvements over strong baselines, with GCAD achieving a 15% reduction in Chamfer distance and a 10% increase in F-score on ShapeNet. The adaptive scheduling mechanism also ensures a better balance between quality and computational efficiency. Our findings substantiate the effectiveness of geometry-complexity-aware adaptive refinement in enhancing reconstruction accuracy and geometric consistency. Our source codes are available at <a href="https://github.com/kamiya1001hana/GCAD.git">https://github.com/kamiya1001hana/GCAD.git</a></p>

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Adaptive diffusion refinement for enhanced 3D surface reconstruction under geometric complexity

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Fei Chen,
  • Ying He,
  • Xiantao Chen,
  • Gong He,
  • Bingcai Chen

摘要

Three-dimensional point cloud reconstruction aims to recover continuous, geometrically consistent surfaces from sparse, unstructured samples, playing a pivotal role in 3D perception and modeling systems. Traditional methods often struggle with spatial nonuniformity and abrupt geometric changes, leading to over-smoothing or artifacts. To address these challenges, we introduce a geometry-complexity-aware diffusion (GCAD) framework that employs diffusion as a region-adaptive feature refiner. This framework utilizes regional complexity scores to drive adaptive timestep allocation and integrates multiscale features via a confidence-aware fusion module, improving stability and consistency under sparse and noisy inputs. Experiments on ShapeNet, SyntheticRoom, and ScanNet demonstrate consistent improvements over strong baselines, with GCAD achieving a 15% reduction in Chamfer distance and a 10% increase in F-score on ShapeNet. The adaptive scheduling mechanism also ensures a better balance between quality and computational efficiency. Our findings substantiate the effectiveness of geometry-complexity-aware adaptive refinement in enhancing reconstruction accuracy and geometric consistency. Our source codes are available at https://github.com/kamiya1001hana/GCAD.git