Sparse and noisy LiDAR completion with RGB guidance and uncertainty (1902.05356v1)

Abstract: This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped to a 2D plane. We propose a new framework which extracts both global and local information in order to produce proper depth maps. We argue that simple depth completion does not require a deep network. However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input. This improves the accuracy significantly. Moreover, confidence masks are exploited in order to take into account the uncertainty in the depth predictions from each modality. This fusion method outperforms the state-of-the-art and ranks first on the KITTI depth completion benchmark. Our code with visualizations is available.

• The paper presents a novel framework that fuses global and local information from LiDAR and RGB data to achieve accurate depth completion.
• It employs unsupervised learning to generate confidence maps that weight predictions, enhancing robustness despite sparse inputs.
• Benchmark tests on the KITTI dataset show state-of-the-art performance, proving its viability for autonomous systems.

Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty

The paper "Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty" presents a sophisticated framework for improving depth predictions in sparse LiDAR maps using RGB guidance. The focus of this research is on providing precise depth predictions, which are critical for applications like autonomous vehicles and robotic-based systems.

Summary and Contributions

The authors introduce a novel framework for generating dense depth predictions from sparse and irregular point clouds, specifically emphasizing the fusion of LiDAR data with RGB images. They contend that precise depth completion does not inherently require a deep network. Instead, they propose a method that integrates both global and local information to accurately predict and amend the sparse input. Key aspects include:

1. Integration of Global and Local Information: The framework combines global and local data, where monocular RGB images serve as guidance. This aspect is crucial for correcting errors and enhancing accuracy in depth completion.
2. Unsupervised Learning of Confidence Maps: The authors present a method to learn confidence maps for both global and local branches in an unsupervised manner. Confidence maps are employed to weight the predicted depth maps according to their reliability, supporting a late fusion approach.
3. Benchmark Performance: The framework significantly surpasses the state-of-the-art, ranking first on the KITTI depth completion benchmark. This excellence is achieved with or without the support of RGB images and without needing additional data or postprocessing methods.

The framework comprises a global branch, which acts as a prior for regularization, and a local branch tasked with detailed depth completion. By leveraging uncertainty maps, the framework determines the areas where global information should supersede local data due to inaccuracies or scant LiDAR points. This combination advances the accuracy of depth predictions significantly.

Theoretical and Practical Implications

Theoretical implications of this paper suggest that global and local information fusion, supported by learned uncertainty maps, presents an efficient strategy for overcoming obstacles related to sparse input data in depth completion tasks. This methodology can have far-reaching applications in any domain that relies on LiDAR-generated point clouds for depth perception, including but not limited to autonomous navigation, robotics, and advanced manufacturing.

Practically, the fusion method outlined in this research has the potential to redefine depth completion techniques by demonstrating that deep networks, while effective, are not always necessary when leveraging well-designed fusion strategies. This revelation could lead to the development of more computationally efficient models that still achieve high levels of accuracy.

Future Directions

Future research could focus on extending this framework to accommodate different sensor modalities or evaluating its applicability in diverse environmental conditions with varying levels of data richness. Moreover, exploring further optimizations in the learning of confidence maps and considering adversarial scenarios where LiDAR data might be intentionally manipulated could yield valuable insights. Continual refinement of these techniques is likely to foster advancements in the fields of computer vision and autonomous system design, ultimately promoting the broader adoption of robust depth completion methods.

In conclusion, the paper illustrates a promising avenue for improving depth completion techniques by emphasizing the fusion of LiDAR and RGB data guided by uncertainty mappings, setting a new standard in real-time and accurate depth estimation.