SAMPart3D: Segment Any Part in 3D Objects (2411.07184v2)

Abstract: 3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision LLMs (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these methods are limited by their reliance on text prompts, which restricts the scalability to large-scale unlabeled datasets and the flexibility in handling part ambiguities. In this work, we introduce SAMPart3D, a scalable zero-shot 3D part segmentation framework that segments any 3D object into semantic parts at multiple granularities, without requiring predefined part label sets as text prompts. For scalability, we use text-agnostic vision foundation models to distill a 3D feature extraction backbone, allowing scaling to large unlabeled 3D datasets to learn rich 3D priors. For flexibility, we distill scale-conditioned part-aware 3D features for 3D part segmentation at multiple granularities. Once the segmented parts are obtained from the scale-conditioned part-aware 3D features, we use VLMs to assign semantic labels to each part based on the multi-view renderings. Compared to previous methods, our SAMPart3D can scale to the recent large-scale 3D object dataset Objaverse and handle complex, non-ordinary objects. Additionally, we contribute a new 3D part segmentation benchmark to address the lack of diversity and complexity of objects and parts in existing benchmarks. Experiments show that our SAMPart3D significantly outperforms existing zero-shot 3D part segmentation methods, and can facilitate various applications such as part-level editing and interactive segmentation.

An Analytical Overview of SAMPart3D: Segment Any Part in 3D Objects

The paper "SAMPart3D: Segment Any Part in 3D Objects" presents a novel framework aimed at addressing the complexities of 3D part segmentation. This research introduces a scalable zero-shot approach for the semantic segmentation of 3D objects into their constituent parts, eliminating the dependence on predefined part label sets or text prompts, thus increasing both scalability and flexibility.

Core Contributions

SAMPart3D introduces significant improvements in 3D object segmentation through the following contributions:

• Zero-shot 3D Part Segmentation Framework: The approach enables segmentation across multiple levels of granularity while eliminating the need for predefined part labels or prompts. This is achieved by leveraging a scale-conditioned MLP for creating granularity-controllable segmentations.
• Text-Independent 2D-to-3D Feature Distillation: By utilizing DINOv2 for visual feature extraction, SAMPart3D effectively distills pertinent 2D features into a 3D context, significantly benefiting from large-scale, unlabeled 3D datasets. This bypasses the reliance on past methods, which suffered from scalability issues due to requirements for text-dependent vision-LLMs.
• Introduction of PartObjaverse-Tiny Dataset: This dataset provides a new benchmark with comprehensive annotations of semantic and instance-level segments, fostering future research with a focus on more diversified and complex 3D object datasets.

Methodological Innovation

The framework utilizes a 3D feature extraction backbone trained through 2D-to-3D feature distillation. The SAMPart3D pipeline integrates several stages:

1. Large-Scale Pretraining: The pretraining phase leverages Objaverse, a massive collection of 3D objects, utilizing DINOv2 to facilitate extrapolation from 2D to 3D features.
2. Scale-Conditioned Grouping: This involves a distinctive approach to handle segmentation granularity using SAM's 2D mask output integrated with scale-conditioned MLPs.
3. Semantic Querying with Multimodal LLMs (MLLMs): By rendering multi-view images and employing MLLMs, SAMPart3D assigns semantic labels to segmented parts, ensuring detailed and coherent segmentation results.

Results and Evaluation

The paper provides an extensive evaluation using the PartObjaverse-Tiny dataset, showcasing superior performance over other zero-shot methods like PointCLIP, PartSLIP, and SAM3D in both semantic and instance segmentation tasks. The use of class-agnostic mIoU as an evaluation metric allows for a nuanced understanding of the segmentation quality. Results indicate that SAMPart3D sets a new benchmark in part-level segmentation adaptability and accuracy, particularly in handling complex and diverse 3D datasets.

Implications and Future Prospects

By innovatively merging 2D and 3D models and allowing for zero-shot segmentation, SAMPart3D pushes forward the capabilities in 3D perception tasks. The implications for real-world applications are vast — from enhancing robotic manipulation to enabling advanced 3D editing pipelines. Furthermore, the modular design promotes versatility in applications such as part-level material editing, animation, and interactive hierarchical segmentation.

Moving ahead, the research opens avenues for further refining zero-shot methods and cultivating more expansive 3D datasets with higher granularity and scope diversity. There is significant potential for exploring advanced model architectures that can further streamline and automate the feature distillation process, perhaps integrating contemporary advancements in AI model efficiency.

In summary, SAMPart3D leverages a creative and efficient design to address intricate challenges in 3D part segmentation. It sets the stage for future developments by harmoniously integrating multi-modal inputs and providing a robust framework adaptable to a plethora of applications.