A Continuous-Time Consistency Model for 3D Point Cloud Generation

• URL: http://arxiv.org/abs/2509.01492v1
• Date: Mon, 01 Sep 2025 14:11:59 GMT
• Title: A Continuous-Time Consistency Model for 3D Point Cloud Generation
• Authors: Sebastian Eilermann, René Heesch, Oliver Niggemann,
• Abstract summary: We introduce ConTiCoM-3D, a continuous-time consistency model that synthesizes 3D directly in point space.<n>The method integrates a TrigFlow-inspired continuous noise schedule with a Chamfer Distance-based geometric loss.<n> Experiments on the ShapeNet benchmark show that ConTiCoM-3D matches or outperforms state-of-the-art diffusion and latent consistency models in both quality and efficiency.
• Score: 0.6308539010172308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fast and accurate 3D shape generation from point clouds is essential for applications in robotics, AR/VR, and digital content creation. We introduce ConTiCoM-3D, a continuous-time consistency model that synthesizes 3D shapes directly in point space, without discretized diffusion steps, pre-trained teacher models, or latent-space encodings. The method integrates a TrigFlow-inspired continuous noise schedule with a Chamfer Distance-based geometric loss, enabling stable training on high-dimensional point sets while avoiding expensive Jacobian-vector products. This design supports efficient one- to two-step inference with high geometric fidelity. In contrast to previous approaches that rely on iterative denoising or latent decoders, ConTiCoM-3D employs a time-conditioned neural network operating entirely in continuous time, thereby achieving fast generation. Experiments on the ShapeNet benchmark show that ConTiCoM-3D matches or outperforms state-of-the-art diffusion and latent consistency models in both quality and efficiency, establishing it as a practical framework for scalable 3D shape generation.

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