Stochastic Morphogenetic Extensions of Autonomous Fractal Trees in D-Dimensional Spaces and Their Optical Rendering via Cascaded Perspective Projection with Lipschitzian Culling Stabilization

Traditional algorithmic frameworks for morphological synthesis, such as discrete L-systems, frequently induce structural dissociation under stochastic perturbation regimes due to the absence of geometric inheritance across ancestral lineages. This paper employs a novel algebraic formulation that models autonomous fractal trees within continuous D-dimensional spaces via cascaded non-linear perspective projections. We extend this deterministic kinematic core to a continuous-space discrete-time Gauss-Markov process, where directional perturbations are dynamically injected into the angular arguments of composite Givens rotation matrices. By evaluating non-commutative matrix products, structural memory is preserved down the lineage while keeping growing edge variations strictly localized to terminal segments. To safeguard computational efficiency under intense noise regimes, we establish a conservative spatial safety margin governed by an analytic Lipschitzian bounding operator. This constraint dynamically widens the clipping windows of conditional intersection operators, enabling a robust early-exit culling mechanism that prunes non-intersecting subtrees in hardware runtime without triggering false-positive branch prunings. Visual and mathematical synchronization is validated via an interactive multi-viewport pipeline spanning native R^4 spaces down to physical 2D terminal canvases, demonstrating a strict O(D^2 * M^(N_max)) complexity bound under stochastic stabilization.