[
    {
        "id": "osp-24081",
        "type": "article-journal",
        "title": "OptimusMesh: Compact Autoregressive Mesh Generation from Point Clouds via Sparse Latent Pivots",
        "author": [
            {
                "family": "Iqbal",
                "given": "Mazhar"
            },
            {
                "family": "Sha",
                "given": "Xuanmeng"
            },
            {
                "family": "Chiba",
                "given": "Naoya"
            },
            {
                "family": "Uranishi",
                "given": "Yuki"
            },
            {
                "family": "Mashita",
                "given": "Tomohiro"
            }
        ],
        "URL": "https://omanscience.com/en/articles/optimusmesh-compact-autoregressive-mesh-generation-from-point-clouds-via-sparse-latent-pivots",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Generating compact and geometrically faithful 3D meshes directly from point clouds remains a fundamental challenge. Point clouds are unordered and sparse, whereas meshes exhibit irregular structure and varying topology. As a result, many existing approaches rely on implicit representations followed by surface extraction or reconstruction. Although effective, these pipelines can produce dense or over-smoothed meshes, often requiring computationally expensive post-processing and simplification. We present OptimusMesh, a framework for direct compact triangle mesh generation from point clouds using sparse latent pivot conditioning. Our key idea is to compress $2{,}048$ oriented input points into only $16$ sparse latent pivots, reducing the geometric conditioning set by $128\\times$. These pivots provide a compact structural representation shared across a two-stage autoregressive framework that first generates mesh vertices and then predicts triangular faces conditioned on the generated vertices and the same pivots. Compared with the evaluated recent point-cloud-conditioned autoregressive methods, which use $257$ decoder-conditioning tokens, OptimusMesh uses only $16$, yielding a $16.1\\times$ shorter conditioning sequence. Experiments show that OptimusMesh produces the most compact outputs among the compared recent autoregressive methods, using $25.7\\%$--$94.1\\%$ fewer faces while maintaining competitive geometric fidelity and distributional quality."
    }
]