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Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features (2311.17024v2)

Published 28 Nov 2023 in cs.CV and cs.GR

Abstract: We present Diff3F as a simple, robust, and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds). Our method distills diffusion features from image foundational models onto input shapes. Specifically, we use the input shapes to produce depth and normal maps as guidance for conditional image synthesis. In the process, we produce (diffusion) features in 2D that we subsequently lift and aggregate on the original surface. Our key observation is that even if the conditional image generations obtained from multi-view rendering of the input shapes are inconsistent, the associated image features are robust and, hence, can be directly aggregated across views. This produces semantic features on the input shapes, without requiring additional data or training. We perform extensive experiments on multiple benchmarks (SHREC'19, SHREC'20, FAUST, and TOSCA) and demonstrate that our features, being semantic instead of geometric, produce reliable correspondence across both isometric and non-isometrically related shape families. Code is available via the project page at https://diff3f.github.io/

Citations (8)

Summary

  • The paper introduces Diff3F, which transfers semantic features from 2D diffusion models to annotate untextured 3D shapes without extra training data.
  • It employs multi-view rendering with depth and normal mappings and uses ControlNet for effective image conditioning during feature aggregation.
  • Diff3F achieves a 26.41% correspondence accuracy on SHREC'19 at a strict 1% error tolerance, outperforming traditional geometric descriptor methods.

An Analysis of Diffusion 3D Features: Enhancing Untextured 3D Shapes with Distilled Semantic Features

The paper introduces Diff3F, a novel framework for generating semantic features on untextured 3D shapes, distinguished for its applicational versatility across varying input modalities such as point clouds and non-manifold meshes. By leveraging the generative capabilities of foundational image models, specifically diffusion models like Stable Diffusion, Diff3F circumvents the challenges typically encountered in texturing untextured shapes without the need for additional training data or optimization processes.

Core Contributions and Methodology

The core contribution of this paper lies in its ability to distill semantic features from pre-trained image diffusion models onto 3D geometric data. Through a process of multi-view rendering and image conditioning using techniques such as ControlNet, the framework extracts diffusion features during the image synthesis process and subsequently aggregates them back to the 3D surfaces. This paves the way for semantic annotation of 3D shapes without the complexities and limitations of classical geometry-based feature extraction methods.

Distillation from 2D to 3D is achieved by projecting 3D shapes onto 2D space via depth and normal mappings, transforming them into colored renderings employing text prompts. The robustness of Diff3F largely derives from aggregating features across multiple views while maintaining consistency, producing descriptors that outperform traditional geometric descriptors in cross-domain correspondence tasks.

Evaluation Metrics and Results

The efficacy of Diff3F is validated across several challenging benchmarks (SHREC'19, SHREC'20, and TOSCA), where it demonstrates superior generalizability and accuracy in establishing correspondences across isometric and non-isometric shapes. Notably, it achieves a correspondence accuracy of 26.41% on the SHREC'19 dataset at a strict error tolerance of 1%, signifying a significant advancement over contemporary unsupervised methods like DPC and SE-ORNet.

Implications and Future Work

The implications of this research are substantial, particularly in fields requiring accurate shape analysis and correspondence, such as computer graphics, virtual reality, and robotics. The ability to compute dense semantic correspondence without additional training data presents a versatile tool in degenerate cases where traditional approaches falter.

The limitation of Diff3F, as outlined by the authors, pertains to its reliance on visibility during multi-view rendering, which can obscure certain surface details due to self-occlusion. Addressing such challenges in future work could involve integrating geometric smoothness energies to enhance robustness against noise. Additionally, the potential to expand these descriptors for volumetric inputs like NeRFs or distance fields offers intriguing avenues for continued research.

Overall, Diff3F showcases how leveraging generative models from the 2D domain can effectively transfer semantic understanding to 3D data, forming a promising frontier for future exploration in artificial intelligence and shape analysis. This paper outlines a method that brings a new semantic dimension to the predominantly geometric world of 3D feature extraction, offering insightful perspectives that could redefine workflows in shape analysis tasks.

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