POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images (2401.09413v1)

Abstract: We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images with the objective of enabling 3D grounding, segmentation and retrieval of free-form language queries. This is a challenging problem because of the 2D-3D ambiguity and the open-vocabulary nature of the target tasks, where obtaining annotated training data in 3D is difficult. The contributions of this work are three-fold. First, we design a new model architecture for open-vocabulary 3D semantic occupancy prediction. The architecture consists of a 2D-3D encoder together with occupancy prediction and 3D-language heads. The output is a dense voxel map of 3D grounded language embeddings enabling a range of open-vocabulary tasks. Second, we develop a tri-modal self-supervised learning algorithm that leverages three modalities: (i) images, (ii) language and (iii) LiDAR point clouds, and enables training the proposed architecture using a strong pre-trained vision-LLM without the need for any 3D manual language annotations. Finally, we demonstrate quantitatively the strengths of the proposed model on several open-vocabulary tasks: Zero-shot 3D semantic segmentation using existing datasets; 3D grounding and retrieval of free-form language queries, using a small dataset that we propose as an extension of nuScenes. You can find the project page here https://vobecant.github.io/POP3D.

• The paper introduces a novel architecture that integrates 2D image features, LiDAR data, and language embeddings for 3D semantic occupancy prediction.
• It employs tri-modal self-supervised learning to distill 2D knowledge into the 3D space, reducing reliance on dense 3D annotations.
• Results show robust zero-shot performance in semantic segmentation and language-driven 3D grounding on the nuScenes dataset.

An Introduction to POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images

The paper "POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images" offers significant insights and contributions to the domain of 3D scene understanding through the lens of open-vocabulary frameworks. This work aims at extending 2D image-based data to 3D voxel predictions, which are crucial for applications such as autonomous driving, augmented reality, and robotics. The authors tackle a core issue in this area, that being the 2D-3D ambiguity coupled with the open-vocabulary challenge, to propose a novel approach that substantially alleviates the problems posed by these aspects without requiring dense 3D annotations.

Contributions and Model Architecture

The POP-3D approach makes three pivotal contributions:

1. Model Architecture: POP-3D introduces an innovative architecture tailored for open-vocabulary 3D semantic occupancy prediction. This involves a 2D-3D encoder incorporated with two heads: an occupancy prediction head and a 3D-language feature extraction head. The design enables the model to output a dense voxel map with grounded language embeddings, facilitating open-vocabulary tasks.
2. Tri-Modal Self-Supervised Learning: The learning paradigm is enriched through a self-supervised mechanism that integrates images, language, and LiDAR point cloud data. This allows the model to leverage a pre-trained vision-LLM effectively, bypassing the need for explicit 3D language annotations, which are cumbersome to obtain.
3. Quantitative Validation: The model demonstrates its proficiency through comprehensive quantitative evaluations on various open-vocabulary tasks. Notably, POP-3D exhibits robust performance in zero-shot 3D semantic segmentation and language-driven 3D grounding and retrieval, using an extended subset of the nuScenes dataset.

Methodology

The methodological backbone of POP-3D revolves around leveraging a pre-trained image-language alignment model, specifically employing CLIP due to its zero-shot generalization capabilities. Through a clever distillation process, knowledge from 2D image spaces is brought into the 3D occupancy field, a task traditionally plagued by the requirement of extensive 3D ground-truth data. The architecture seamlessly combines 2D information with LiDAR data, resulting in a rich feature space that allows for open-vocabulary querying in 3D.

Results and Evaluation Protocol

Experimentation on nuScenes, a comprehensive dataset for autonomous driving, affirms the efficacy of the proposed approach. POP-3D successfully advances the state-of-the-art methodologies in terms of semantic occupancy prediction without relying on 3D annotations. Results indicate that POP-3D can achieve approximately 78% of the performance of fully-supervised counterparts in semantic segmentation and also surpass MaskCLIP in 3D feature learning benchmarks.

Implications and Future Directions

From a practical standpoint, the implications of POP-3D are manifold. In autonomous systems, the ability to predict 3D environments from mere 2D visual cues effectively reduces the dependency on costly and complex sensing architectures. The work propels forward the notion of open-vocabulary tasks in 3D space, thereby promoting a more scalable and richly descriptive understanding of 3D scenes.

Theoretically, POP-3D underscores the potential unlocked by aligning multi-modal feature spaces, establishing a new avenue for language and vision interfacing within 3D realms. The integration of language prompts provides an elegant mechanism for refining feature querying and enabling latent space exploration.

While POP-3D sets a precedent in open-vocabulary 3D geometrics, future explorations could focus on enhancing voxel resolution, refining real-time processing for dynamic scenes, and investigating deeper integration of temporal data to handle motion and occlusion challenges effectively. The continuous evolution of vision-LLMs promises further advancements in crafting enriched, context-aware 3D representations.

In conclusion, POP-3D marks a significant step toward understanding and interpreting 3D environments from the rich but ambiguous visual-world descriptions, providing a robust framework that merges state-of-the-art machine learning practices with applied computer vision challenges.