N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields (2403.10997v2)

Abstract: Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision. To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properties at varying granularities. Our method allows for a flexible definition of hierarchies, tailored to either the physical dimensions or semantics or both, thereby enabling a comprehensive and nuanced understanding of scenes. We leverage a 2D class-agnostic segmentation model to provide semantically meaningful pixel groupings at arbitrary scales in the image space, and query the CLIP vision-encoder to obtain language-aligned embeddings for each of these segments. Our proposed hierarchical supervision method then assigns different nested dimensions of the feature field to distill the CLIP embeddings using deferred volumetric rendering at varying physical scales, creating a coarse-to-fine representation. Extensive experiments show that our approach outperforms the state-of-the-art feature field distillation methods on tasks such as open-vocabulary 3D segmentation and localization, demonstrating the effectiveness of the learned nested feature field.

• The paper introduces N2F2, a framework that hierarchically encodes 3D scenes with nested neural feature fields capturing both global and detailed semantics.
• It leverages a 3D Gaussian Splatting method with SAM segmentation and CLIP embeddings to integrate geometric and semantic details across scales.
• Experimental results show N2F2 outperforms prior methods, achieving 1.7x faster scene querying and improved open-vocabulary segmentation and localization.

Analyzing "N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields"

The paper "N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields" introduces a novel framework for hierarchical scene understanding in the domain of computer vision. The primary contribution of this work is the development of Nested Neural Feature Fields (N2F2), leveraging hierarchical supervision to learn feature fields that encode scene properties at various granularities. This is particularly significant as it enables the simultaneous comprehension of a scene at both macro and micro levels, addressing a fundamental challenge in scene understanding.

Core Methodology

The N2F2 approach is designed to handle the complexity of 3D scenes by embedding semantics into a single, multiscale feature field, termed Nested Neural Feature Fields. This is achieved through a 3D Gaussian Splatting method which efficiently captures both geometric and semantic information from multiple viewpoints. To extract semantic details, the framework utilizes the Segment Anything Model (SAM) for segmenting the scene, allowing the features to align with language representations derived from CLIP embeddings via a vision encoder.

Hierarchical supervision is a key innovation in this research. The feature field learns to encode different levels of granularity through supervised learning at scales defined by quantized measures. Such scales can be thwarted with nested dimensions, providing a diverse representation that captures both broad and intricate details of the objects and scenes.

Experimental Analysis

The authors report performance improvements over state-of-the-art methods on tasks including open-vocabulary 3D segmentation and localization. The experiments, conducted primarily on datasets that include scenes with diverse and complex queries, demonstrate that N2F2 exceeds previous benchmarks. Specifically, it shows enhanced capability in decoding complex scenes with compositional queries like compound nouns and partitive phrases, which are generally challenging for existing models. For instance, complex queries like "bag of cookies" or "chocolate donut" are tackled efficiently, highlighting the robustness of the hierarchical framework. It is noted that N2F2 outperforms previous method LangSplat by being 1.7 times faster, allowing for rapid scene querying.

Implications and Future Directions

Practically, the implications of this paper are substantial for domains requiring nuanced interactions with 3D environments, such as robotics and augmented reality. The ability for models to understand hierarchically organized semantics can enhance the precision and depth at which machines interpret their surroundings. Theoretically, this work contributes to the ongoing development of neural fields by demonstrating the potential of hierarchical architectures to enrich scene interpretation, inviting further exploration into multiscale representation learning.

Future avenues suggested by the authors include refining the model's ability to process global context queries to further its applicability in vast, unbounded environments. Moreover, integrating the N2F2 framework within existing or novel AI architectures could open pathways for more sophisticated scene understanding technologies.

In conclusion, "N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields" presents a significant advancement in computer vision, particularly in the domain of hierarchical understanding. The integration of nested feature fields, combined with efficient rendering techniques and comprehensive segmentation models, sets a new standard for open-vocabulary 3D scene representation and interpretation.