OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views (2404.03650v1)

Abstract: Large visual-LLMs (VLMs), like CLIP, enable open-set image segmentation to segment arbitrary concepts from an image in a zero-shot manner. This goes beyond the traditional closed-set assumption, i.e., where models can only segment classes from a pre-defined training set. More recently, first works on open-set segmentation in 3D scenes have appeared in the literature. These methods are heavily influenced by closed-set 3D convolutional approaches that process point clouds or polygon meshes. However, these 3D scene representations do not align well with the image-based nature of the visual-LLMs. Indeed, point cloud and 3D meshes typically have a lower resolution than images and the reconstructed 3D scene geometry might not project well to the underlying 2D image sequences used to compute pixel-aligned CLIP features. To address these challenges, we propose OpenNeRF which naturally operates on posed images and directly encodes the VLM features within the NeRF. This is similar in spirit to LERF, however our work shows that using pixel-wise VLM features (instead of global CLIP features) results in an overall less complex architecture without the need for additional DINO regularization. Our OpenNeRF further leverages NeRF's ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images. For 3D point cloud segmentation on the Replica dataset, OpenNeRF outperforms recent open-vocabulary methods such as LERF and OpenScene by at least +4.9 mIoU.

OpenNeRF: Advancements in Open Set 3D Neural Scene Segmentation

The paper "OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views" introduces a method aimed at improving the capabilities of 3D scene segmentation through an innovative approach that leverages neural radiance fields (NeRF) in conjunction with pixel-aligned visual-LLM (VLM) features. This research provides a meaningful contribution to the field of open-set 3D scene understanding by addressing the constraints associated with traditional closed-set models and existing open vocabulary methods.

Core Contributions

The authors propose OpenNeRF, a novel approach that integrates pixel-wise VLM features within NeRF. This method is notably distinct from previous techniques like LERF, which rely on global CLIP features. By focusing on pixel-level detail, OpenNeRF enhances the precision of semantic segmentation without the architectural complexities introduced by other regularization strategies, such as DINO.

OpenNeRF's design leverages the inherent strengths of NeRF, particularly its capacity to render novel views. This ability is utilized to extract VLM features from areas inadequately represented in the initial dataset of posed images. The ability to generate novel views is harnessed through a probabilistic mechanism that intelligently determines which areas of the scene necessitate additional camera perspectives, thereby refining the segmentation process.

Evaluation and Results

The paper presents empirical evidence showcasing that OpenNeRF achieves a significant improvement over other methods like LERF and OpenScene in 3D point cloud segmentation tasks. Specifically, on the Replica dataset, OpenNeRF surpasses recent open-vocabulary methods by at least a 4.9 point increase in mean Intersection over Union (mIoU). This substantial metric indicates better performance in terms of accuracy and consistency in segmenting arbitrary, open-set concepts within 3D scenes.

Implications and Future Directions

OpenNeRF's success implies considerable potential for applications in augmented reality (AR), virtual reality (VR), robotic perception, and autonomous driving—domains where a fine-grained understanding of complex environments is essential. The framework's open-set approach facilitates adaptation to novel semantic classes, which is crucial for systems that operate in dynamic and unstructured environments.

For theoretical exploration, the integration of pixel-aligned VLM features with NeRF could pave the way for more sophisticated representations of three-dimensional spaces, enabling advancements in a variety of machine perception tasks. Future research may focus on the exploration of NeRF’s capabilities in handling more diverse and larger-scale datasets, optimizing the rendering of novel views, and examining the use of other types of embeddings in enhancing the semantic understanding of scenes.

The paper presents a meaningful step forward in open-set 3D scene segmentation and offers a foundation for further innovation in neural scene representation technologies. Its contributions hold promise for both enhancing existing systems and inspiring new methodologies within the broader computational and perceptual research communities.