Search arXivSearch

arXiv · 2608.28982

FractureFields: Contact-Aware Binary Multi-Field Transfer for Fractured 3D Gaussian Simulation

Abstract

Physics-integrated 3D Gaussian representations make it possible to simulate image-reconstructed assets directly as particles, but current Gaussia-MPM pipelines keep a single Eulerian velocity field even after fracture. When disconnected fragments share interpolation support, they still write to and read from the same grid nodes, producing cross-fragment momentum leakage that appears as residual adhesion and non-physical stretching. We present FractureFields, a topology-adaptive transfer for fractured 3D Gaussian objects. After a structural event assigns persistent fragment identities, FractureFields builds fragment-specific mass and momentum fields in a single P2G pass, advances each field independently, and performs a field-aware G2P update so particles only sample their own fragment's grid state. To handle re-contact, we add a momentum-conserving contact projection that applies equal and opposite normal impulses only when two fragment fields are approaching, preserving free separation otherwise. Experiments on reconstructed scenes and a controlled re-contact benchmark show that fragment-conditioned routing eliminates realized cross-fragment mixing by construction, while contact projection reduces interpenetration during collision without reintroducing residual coupling. Overall, we argue that post-fracture simulation should treat structural disconnection as a change in local dynamical state, not merely a change in constitutive stress.

Explore related subjects

Keep this discovery

BibTeXRIS

Jianchen Wang, Runyang Qu, Fei Li. 2026-08-29. FractureFields: Contact-Aware Binary Multi-Field Transfer for Fractured 3D Gaussian Simulation. https://arxiv.org/abs/2608.28982

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

Stochastic Optimization of Tree Tensor Networks

Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optimizers for tree tensor networks (TTNs) on both their parameter and quotient manifolds, including adaptive and learning-rate-free schemes suitable for minibatch training. Using a hybrid CNN-TTN architecture, we evaluate the methods on Fashion-MNIST, CIFAR10, and Imagenette. The proposed optimizers achieve predictive performance comparable to unconstrained optimization while enabling numerically stable downstream compression.

math.OC

Texture Image Classification Using DWT AlexNet Feature Fusion and Deep Neural Networks

Texture image classification plays a significant role in computer vision applications, including industrial inspection, medical image analysis, remote sensing, and object recognition. Handcrafted features can capture local texture characteristics but may have limited capability to represent complex visual patterns. In contrast, deep learning models automatically learn discriminative representations but may not fully exploit the multiscale spatial-frequency information inherent in texture images. This paper proposes a hybrid feature fusion framework, termed DWT_AlexNet_DNN, which combines Discrete Wavelet Transform (DWT) features with deep features extracted using AlexNet for texture image classification.

cs.CV

GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

Conventionally, Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation models. In this work, we ask whether a general-purpose Vision-Language Model (VLM) can localize buildings and grade their damage through autoregressive sequence generation alone. We cast BDA as predicting a variable-length set of bounding boxes, each specified by its coordinates and a damage label. Our preliminary implementation, based on the open Gemma model, achieves promising damage mapping results from only bi-temporal satellite images and a suitable text prompt.

cs.CV