Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion (2303.13959v6)

Abstract: 3D semantic scene completion (SSC) is an ill-posed perception task that requires inferring a dense 3D scene from limited observations. Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations. In this paper, we resort to stereo matching technique and bird's-eye-view (BEV) representation learning to address such issues in SSC. Complementary to each other, stereo matching mitigates geometric ambiguity with epipolar constraint while BEV representation enhances the hallucination ability for invisible regions with global semantic context. However, due to the inherent representation gap between stereo geometry and BEV features, it is non-trivial to bridge them for dense prediction task of SSC. Therefore, we further develop a unified occupancy-based framework dubbed BRGScene, which effectively bridges these two representations with dense 3D volumes for reliable semantic scene completion. Specifically, we design a novel Mutual Interactive Ensemble (MIE) block for pixel-level reliable aggregation of stereo geometry and BEV features. Within the MIE block, a Bi-directional Reliable Interaction (BRI) module, enhanced with confidence re-weighting, is employed to encourage fine-grained interaction through mutual guidance. Besides, a Dual Volume Ensemble (DVE) module is introduced to facilitate complementary aggregation through channel-wise recalibration and multi-group voting. Our method outperforms all published camera-based methods on SemanticKITTI for semantic scene completion. Our code is available on https://github.com/Arlo0o/StereoScene.

• The paper introduces BRGScene, a framework that unifies stereo geometry and BEV features for effective 3D semantic scene completion from camera inputs.
• The paper employs a Mutual Interactive Ensemble block with BRI and DVE modules to enable bi-directional feature interaction and precise multi-scale context aggregation.
• The paper demonstrates a 14.5% mIoU improvement on the SemanticKITTI benchmark, highlighting its potential for cost-effective autonomous and robotics applications.

Bridging Stereo Geometry and BEV Representation for Semantic Scene Completion

The paper "Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion" addresses a critical challenge in computer vision: achieving 3D semantic scene completion (SSC) using cost-effective camera inputs rather than expensive sensors like LiDAR. This problem is inherently complex due to the ill-posed nature of inferring detailed 3D structures from 2D image data. The authors present a novel framework, BRGScene, which leverages both stereo matching and bird's-eye-view (BEV) representation to overcome the inherent geometric ambiguities and limited observations typically encountered in camera-based approaches.

Framework Overview

BRGScene introduces a unified occupancy-based framework that combines stereo geometry with BEV features, significantly improving the ability to discern both geometric and semantic elements of a scene in a dense 3D context. The key innovation here is the Mutual Interactive Ensemble (MIE) block, which integrates stereo and BEV features through a bi-directional reliable interaction. This mechanism ensures fine-grained information exchange, enhancing the model's capacity to make reliable predictions even in scenarios with significant occlusion or reflection challenges.

Two primary components of the MIE block are the Bi-directional Reliable Interaction (BRI) module and the Dual Volume Ensemble (DVE) module. The BRI module utilizes a confidence re-weighting strategy inspired by multi-view stereo (MVS) techniques to encourage precise stereo and BEV feature integration. Concurrently, the DVE module facilitates the complementary consolidation of stereo and BEV insights through channel-wise recalibration and multi-scale context aggregation.

Strong Numerical Results and Performance

Numerical results indicate that BRGScene outperforms existing camera-based solutions on the SemanticKITTI benchmark, achieving notable improvements in both intersection over union (IoU) for geometric completion and mean intersection over union (mIoU) for semantic segmentation tasks. Specifically, BRGScene yields a relative improvement of 14.5% in mIoU compared to VoxFormer-T, a state-of-the-art model utilizing stereo inputs. This performance gain underscores the effectiveness of the integrated stereo-BEV approach for scene completion.

Implications and Future Directions

The implications of this research are substantial for various applications in autonomous driving, robotics, and augmented reality, where cost-effective 3D scene understanding is paramount. By employing stereo cameras—an economically viable alternative to LiDAR—the proposed framework presents a scalable solution for real-time semantic scene completion with reliable performance across diverse environments.

Theoretically, the paper emphasizes the importance of integrating multi-source representations to address the limitations inherent in single-modality methods. This multi-faceted approach sets a precedent for future research aimed at exploiting complementary cues from different sensory inputs to improve scene understanding accuracy.

Future developments in this domain might explore further optimization of the interaction mechanisms between stereo and BEV features. Additionally, extending this framework to incorporate temporal dynamics could enhance the model's ability to predict scene changes and movements more effectively, thereby broadening its applicability to dynamic environments.

In conclusion, the paper provides a robust framework that bridges the gap between stereo geometry and BEV representation, advancing the state of the art in camera-based 3D semantic scene completion. The integration of advanced interaction modules and the highlighted results pave the way for more comprehensive and accessible solutions in the field of computer vision.