Intersection-Aware Asset Placement using Computational Geometry and ML

Intersection-Aware Asset Placement using Computational Geometry and ML ## Intersection Analysis and Packing ## Direction-Aware Placement ## ML-Based Pipeline Automation ## User Abstraction Layer ## Automated Asset Retrieval ## Future Work <script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script> Most existing engines and DCC tools still lack robust intersection-aware asset placement, even within modern PCG workflows. Manual placement with geometric perturbations remains time-consuming and inefficient. To address this, we introduce a system that combines computational geometry with pipeline-engineering techniques to support large-scale asset processing. Our approach incorporates UV-based and general packing strategies, including Poisson disk sampling and scale-aware classification, expressed using computational geometry rather than conventional algorithmic heuristics. The Poisson disk sampling can be represented as: $$ d(x_i, x_j) \ge r, \quad \forall i \neq j $$ where $r$ is the minimum distance between samples. Scale-aware classification assigns assets to bins based on their bounding box sizes: $$ \text{bin}(i) = \left\lfloor \frac{\text{size}_i}{\Delta} \right\rfloor $$ where $\Delta$ is the scale interval. To support semantic directional awareness, we apply Rodrigues’ rotation formula to align an object’s primary directional vector $\mathbf{v}$ to a target normal $\mathbf{n}$: $$ \mathbf{v}_{\text{rot}} = \mathbf{v} \cos \theta + (\mathbf{k} \times \mathbf{v}) \sin \theta + \mathbf{k} (\mathbf{k} \cdot \mathbf{v}) (1 - \cos \theta) $$ where $\mathbf{k}$ is the unit rotation axis and $\theta$ is the angle between $\mathbf{v}$ and $\mathbf{n}$. Alternatively, rotations are represented with quaternions: $$ \mathbf{v}' = \mathbf{q} \mathbf{v} \mathbf{q}^{-1}, \quad \mathbf{q} = w + xi + yj + zk $$ Matrix transformations are applied using matrix inversion: $$ \mathbf{T}^{-1} = (\mathbf{R} \mathbf{S})^{-1} = \mathbf{S}^{-1} \mathbf{R}^{-1} $$ where $\mathbf{R}$ is the rotation component and $\mathbf{S}$ is scaling. We integrated MCP, an ML-based API, to automate scaling and direction-aware initialization, forming an AI-assisted geometry pipeline. This reduces manual intervention during dataset creation and ensures consistent placement across large-scale assets. A high-level abstraction layer enables: These features are not available in current engines or DCC plugins. We implemented an automated system using the FAB API: Note: FAB recently limited API access for uninitialized users, but authenticated workflows remain functional. A preliminary version of this work is under development as a research paper. Future sections will include: This aims to establish a generalizable and systematic methodology for intersection-aware asset placement. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: v_{rot} = v \cos \theta + (k \times v) \sin \theta + k (k \cdot v) (1 - \cos \theta) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: v_{rot} = v \cos \theta + (k \times v) \sin \theta + k (k \cdot v) (1 - \cos \theta) CODE_BLOCK: v_{rot} = v \cos \theta + (k \times v) \sin \theta + k (k \cdot v) (1 - \cos \theta) - Batch import/export - Structured directory output - Automated PBR-compatible texture assignment - HLSL shader blending - Handling of inconsistent naming conventions - ARM/AO texture linking - Supports token-based bulk downloads by UID or keyword - Saves assets to specified directories via command line - Facilitates efficient dataset creation for 3D geometry research - Multi-DCC engine integration - Performance lookup tables - Analysis of boundary-case behaviors