Structurally Disentangled Feature Fields Distillation for 3D Understanding and Editing

• URL: http://arxiv.org/abs/2502.14789v1
• Date: Thu, 20 Feb 2025 18:09:27 GMT
• Title: Structurally Disentangled Feature Fields Distillation for 3D Understanding and Editing
• Authors: Yoel Levy, David Shavin, Itai Lang, Sagie Benaim,
• Abstract summary: We propose to capture 3D features using multiple disentangled feature fields.<n>Each element can be controlled in isolation, enabling semantic and structural understanding and editing capabilities.
• Score: 14.7298711927857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work has demonstrated the ability to leverage or distill pre-trained 2D features obtained using large pre-trained 2D models into 3D features, enabling impressive 3D editing and understanding capabilities using only 2D supervision. Although impressive, models assume that 3D features are captured using a single feature field and often make a simplifying assumption that features are view-independent. In this work, we propose instead to capture 3D features using multiple disentangled feature fields that capture different structural components of 3D features involving view-dependent and view-independent components, which can be learned from 2D feature supervision only. Subsequently, each element can be controlled in isolation, enabling semantic and structural understanding and editing capabilities. For instance, using a user click, one can segment 3D features corresponding to a given object and then segment, edit, or remove their view-dependent (reflective) properties. We evaluate our approach on the task of 3D segmentation and demonstrate a set of novel understanding and editing tasks.

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