Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs (1512.06735v2)

Abstract: Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [Zhang et al., ICCV15] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in [Zhang et al., ICCV15] to derive a globally consistent instance labeling of the image. In this paper, we formulate the global labeling problem with a novel densely connected Markov random field and show how to encode various intuitive potentials in a way that is amenable to efficient mean field inference [Kr\"ahenb\"uhl et al., NIPS11]. Our potentials encode the compatibility between the global labeling and the patch-level predictions, contrast-sensitive smoothness as well as the fact that separate regions form different instances. Our experiments on the challenging KITTI benchmark [Geiger et al., CVPR12] demonstrate that our method achieves a significant performance boost over the baseline [Zhang et al., ICCV15].

• The paper proposes fusing CNNs with densely connected Markov random fields for accurate pixel-wise instance-level segmentation in autonomous driving.
• It employs a densely connected MRF structure with efficient mean-field inference and specialized potentials to improve label propagation and global consistency.
• Experiments on the KITTI benchmark demonstrate significant performance gains, improving instance discernment in complex urban scenes vital for autonomous driving.

Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs

The paper "Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs" introduces an approach to tackle the pixel-wise instance-level segmentation of monocular images within the context of autonomous driving. The authors employ a fusion of convolutional neural networks (CNNs) with densely connected Markov random fields (MRFs) to derive seamless and coherent instance-level segmentations from local predictions.

Key Contributions and Methodological Framework

The emphasis of this research lies in designing an advanced MRF framework that enables efficient mean-field inference to produce instance-level segmentation maps. The proposed method improves on previous work, notably over the baseline that predicts instance labels using a CNN for image patches and implements an MRF to enforce global consistency. Here, the primary advances are:

1. Densely Connected MRFs: Instead of using simplistic connectivity heuristics, the research introduces a densely connected MRF structure, which embraces dense pairwise pixel connectivity for more robust label propagation. This MRF encodes multiple potentials, addressing smoothness constraints by incorporating contrast-sensitive pairwise connections.
2. Efficient Inference using Mean Field Approximations: The paper leverages efficient inference algorithms suitable for fully connected models, as per prior efficient Gaussian MRFs research. The MRF potentials consider compatibility between global labels and patch-level predictions, emphasizing the need for smooth transitions and ensuring different instances are recognized as separate entities.
3. Incorporation of Local and Global Features: The potentials within the MRF framework are carefully developed to address the connections between local patch-level predictions and global labeling, making it feasible to use the local CNN outputs effectively to enhance global segmentation.

Experimental Outcomes and Results

The research demonstrates its results on the KITTI benchmark for autonomous driving scenarios, confirming a significant improvement over their previous baseline methods. Noteworthy findings include:

• A performance boost in the segmentation accuracy compared to the heuristic methods initially proposed.
• Enhanced capability to discern and retain separate instances within densely packed urban landscapes, addressing problems with occlusion and multi-object clustering.
• Empirical demonstration reveals substantial gains across various metrics, notably in mean weighted coverage and average precision/recall rates, validating the robust performance of the proposed methodology over conventional approaches.

Implications and Future Research Directions

The implications of this research are considerable for the field of autonomous driving, where accurate, instance-level segmentation is crucial for tasks such as obstacle avoidance, navigation, and scene understanding. By employing densely connected MRFs, this work has paved the way for more reliable and precise segmentation techniques capable of adapting to a diverse range of driving environments.

Looking forward, potential future research avenues could investigate:

• Real-Time Efficiency: While the proposed method improves accuracy, integrating enhancements to process these algorithms in real-time would be particularly beneficial for deployment in autonomous vehicles.
• Transferability and Generalization: Extending the applicability of the model to other datasets and environments, examining its ability to generalize across different contexts beyond the KITTI benchmark.
• Integration with Depth Sensors: Since monocular images can be limited by lack of depth, complementing this approach with data from LIDAR or stereo systems may improve its robustness and accuracy.

In summary, this paper presents a methodological advancement in instance-level segmentation tasks, contributing a significant improvement over previous state-of-the-art methods through its use of deep learning and probabilistic graphical models, particularly in complex scenarios inherent to autonomous driving.