Author ORCID Identifier
0009-0009-9364-5900
Document Type
Dissertation
Date of Award
5-31-2026
Degree Name
Doctor of Philosophy in Computing Sciences - (Ph.D.)
Department
Computer Science
First Advisor
Przemyslaw Musialski
Second Advisor
Jason T. L. Wang
Third Advisor
Ioannis Koutis
Fourth Advisor
Tomer Weiss
Fifth Advisor
Przemyslaw Spurek
Abstract
Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.
First, signed distance function (SDF) reconstruction with implicit neural representations is improved through periodic activation functions, positional encodings, and surface-normal guidance in an efficient auto-decoder pipeline. Evaluated on a widely used general 3D benchmark, these design choices improve the recovery of high-frequency detail and sharp features while reducing training cost relative to standard implicit baselines.
Second, a unified framework for CAD-derived geometry is introduced that couples curvature-regularized reconstruction with part-level segmentation in a single continuous field. By attaching a compact segmentation head to the reconstruction network, the framework jointly recovers geometry and coherent part boundaries from sampled carriers derived from CAD models.
Third, STEP-Parts is presented as a deterministic method for extracting stable part structure directly from B-Rep topology and analytic primitive information. Because the resulting partitions are invariant to tessellation choices, STEP-Parts provides reproducible geometric decompositions that support large-scale evaluation and reliable supervision for part-aware learning.
Together, these contributions establish a progression from general neural implicit reconstruction to CAD-native supervision, supporting more accurate and reproducible pipelines for reconstruction, segmentation, and downstream geometric learning on man-made 3D geometry.
Recommended Citation
Fan, Shen, "Toward learning-based reconstruction and part decomposition of man-made 3d geometry: neural implicit representations and scalable supervision" (2026). Dissertations. 1879.
https://digitalcommons.njit.edu/dissertations/1879
Included in
Artificial Intelligence and Robotics Commons, Computer-Aided Engineering and Design Commons, Data Science Commons, Graphics and Human Computer Interfaces Commons
