SAMa: Material-aware 3D Selection and Segmentation (2411.19322v1)

Abstract: Decomposing 3D assets into material parts is a common task for artists and creators, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for various 3D representations. Building on the recently introduced SAM2 video selection model, we extend its capabilities to the material domain. We leverage the model's cross-view consistency to create a 3D-consistent intermediate material-similarity representation in the form of a point cloud from a sparse set of views. Nearest-neighbour lookups in this similarity cloud allow us to efficiently reconstruct accurate continuous selection masks over objects' surfaces that can be inspected from any view. Our method is multiview-consistent by design, alleviating the need for contrastive learning or feature-field pre-processing, and performs optimization-free selection in seconds. Our approach works on arbitrary 3D representations and outperforms several strong baselines in terms of selection accuracy and multiview consistency. It enables several compelling applications, such as replacing the diffuse-textured materials on a text-to-3D output, or selecting and editing materials on NeRFs and 3D-Gaussians.

• The paper introduces SAMa, a method that extends SAM2 for efficient, optimization-free 3D material selection with high multiview consistency.
• It employs a point cloud intermediate representation and nearest-neighbor lookups to accurately reconstruct continuous material masks from sparse views.
• Experimental results show superior mIoU and F1 scores across various datasets, underlining its impact on simplifying 3D content creation workflows.

SAMa: Material-Aware 3D Selection and Segmentation

The paper "SAMa: Material-aware 3D Selection and Segmentation" introduces an innovative approach called SAMa that addresses the automated selection and segmentation of materials on 3D objects. This research effort is a response to the highly manual and time-intensive process of decomposing 3D assets into material parts, a crucial task for artists and creators engaged in digital content creation.

Technical Overview

SAMa extends the capabilities of the existing Segment Anything Model 2 (SAM2) video selection model into the material domain. By leveraging the cross-view consistency intrinsic to SAM2, the researchers developed a method to create a 3D-consistent intermediate material-similarity representation. This representation is constructed as a point cloud derived from a sparse set of views. Nearest-neighbor lookups within this point cloud facilitate the efficient reconstruction of accurate, continuous selection masks across the surfaces of 3D objects, viewable from any angle.

An important feature of SAMa is its intrinsic multiview consistency by design, eliminating the need for contrastive learning or feature-field pre-processing, which are typically required in similar tasks. As a result, the method supports optimization-free selections executed within seconds. The approach is applicable across a variety of 3D representations, including meshes, Neural Radiance Fields (NeRFs), and 3D Gaussians, delivering superior selection accuracy and multiview consistency relative to existing baselines.

Experimental Results

The experimental evaluation underscores the robustness and efficiency of SAMa. Quantitatively, the method performs significantly better than the compared baselines on metrics such as mean Intersection over Union (mIoU) and F1 score, across datasets from NeRF, MIPNeRF-360, and a custom dataset devised by the authors. Specifically, SAMa demonstrated a higher mIoU and F1 across all datasets, reflecting its aptitude for material selection accuracy.

Furthermore, the method shows excellent multiview consistency, with low Hamming distances in cross-view tests, indicating reliable selection performance across different perspectives. In robustness evaluations, SAMa exhibited minimal sensitivity to the positional variations in user input, manifesting stable selection outputs regardless of click location diversity.

Practical Implications and Future Directions

SAMa heralds significant practical applications in 3D content creation and editing, facilitating enhanced X-to-3D workflows with material masks and improving the editability of 3D reconstructions. For instance, the technique enables users to replace or modify materials in text-to-3D generated assets, further extending its utility to tasks such as NeRF and Gaussian editing or automatic mesh segmentation into material IDs.

Theoretically, SAMa’s approach of leveraging video models for multiview consistency without extensive computational overhead presents an attractive direction for future research. Exploring larger-scale datasets for training material selection models could further refine material distinction capabilities, particularly in challenging scenarios such as transparent or reflective materials. Additionally, potential developments in depth estimation accuracy could significantly enhance the precision of 3D selection outcomes by providing more reliable depth data for point cloud construction.

In conclusion, SAMa represents a significant advancement in the domain of 3D material selection and segmentation, combining efficiency and adaptability in a novel framework that promises to support a range of applications and inspire continued exploration in the intersection of 3D computer graphics and machine learning.