BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence (2411.14869v2)

Abstract: In embodied intelligence systems, a key component is 3D perception algorithm, which enables agents to understand their surrounding environments. Previous algorithms primarily rely on point cloud, which, despite offering precise geometric information, still constrain perception performance due to inherent sparsity, noise, and data scarcity. In this work, we introduce a novel image-centric 3D perception model, BIP3D, which leverages expressive image features with explicit 3D position encoding to overcome the limitations of point-centric methods. Specifically, we leverage pre-trained 2D vision foundation models to enhance semantic understanding, and introduce a spatial enhancer module to improve spatial understanding. Together, these modules enable BIP3D to achieve multi-view, multi-modal feature fusion and end-to-end 3D perception. In our experiments, BIP3D outperforms current state-of-the-art results on the EmbodiedScan benchmark, achieving improvements of 5.69% in the 3D detection task and 15.25% in the 3D visual grounding task.

• The paper introduces an image-centric framework integrating 2D vision models with explicit 3D position encoding to enhance embodied intelligence.
• It employs a spatial enhancer module and feature fusion strategies, achieving a 5.69% gain in 3D detection and a 15.25% boost in visual grounding.
• This approach overcomes traditional point cloud limitations, enabling cost-effective 3D perception for robotics, AR, and autonomous navigation.

An Overview of BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence

The paper "BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence" introduces a novel methodology aimed at enhancing the capabilities of embodied intelligence systems through improved 3D perception. The research pivots away from conventional point-centric models, opting instead for an image-centric approach by integrating expressive image features with explicit 3D position encoding. This method mitigates the limitations of sparse, noisy, and data-intensive point clouds traditionally used for 3D perception in autonomous agents.

Core Methodology

BIP3D leverages pre-trained 2D vision foundation models, a strategic decision supported by the rapid advancements and strong semantic capabilities demonstrated in 2D vision models over recent years. The backbone of BIP3D includes a spatial enhancer module, which boosts spatial understanding, and feature fusion strategies comprising multi-view and multi-modal capabilities. Specifically, BIP3D executes 3D perception through a combination of image and text inputs, supplemented by optional depth maps.

This paper embarks on optimizing the feature extraction process using the existing capabilities of 2D vision foundation models (e.g., CLIP, EVA, DINO) to facilitate the migration of expressive features into the 3D domain. The spatial enhancer module embodies the core innovation of BIP3D, explicitly encoding camera models for effective 3D position embedding based on 2D image features and predicted depth distribution.

Empirical Evaluation

Extensive experiments conducted using the EmbodiedScan benchmark reveal BIP3D's superior performance over existing methodologies. The paper reports a 5.69% improvement in the 3D detection task and a remarkable 15.25% enhancement in the 3D visual grounding task over the baseline. This empirical validation underscores BIP3D's efficacy as a state-of-the-art solution in scenarios trained on multi-view image data.

The paper also explores comparative performance metrics across varied object sizes (small, medium, large) and category distributions (head, common, tail), illustrating BIP3D's capability to efficiently handle diverse scenarios and maintain robustness against the complexity of real-world scenes.

Discussion and Implications

BIP3D's inclination towards image-centric data paves the way for scalable and cost-effective data collection methods, vital for continually advancing embodied intelligence applications. This transition leverages the dense informational content inherent in images, countering the challenges posed by sparse point clouds.

The potential implications of BIP3D extend to practical applications in areas such as robotics, augmented reality, and autonomous navigation, where precise 3D perception is crucial. The work demonstrates a shift toward integrating 2D semantic understanding into 3D environments, suggesting that future research might involve exploring dynamic scenes or extending BIP3D's capabilities to tasks like instance segmentation and higher-level cognitive tasks.

Future Directions

The paper opens avenues for further research, including architectural optimizations, expansions to dynamic environments, and tackling additional perception tasks. The emphasis is on broadening BIP3D's applicability and sharpening its integration abilities across various tasks, possibly venturing into vision-language intersections and planning.

In sum, while BIP3D establishes itself as a substantial progression toward unifying 2D and 3D perception capabilities, the paper catalyzes ongoing discourse and exploration in the domain of embodied intelligence, heralding future advancements and adaptations in AI-driven perception systems.