3D Packing for Self-Supervised Monocular Depth Estimation (1905.02693v4)

Abstract: Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception. In this work, we propose a novel self-supervised monocular depth estimation method combining geometry with a new deep network, PackNet, learned only from unlabeled monocular videos. Our architecture leverages novel symmetrical packing and unpacking blocks to jointly learn to compress and decompress detail-preserving representations using 3D convolutions. Although self-supervised, our method outperforms other self, semi, and fully supervised methods on the KITTI benchmark. The 3D inductive bias in PackNet enables it to scale with input resolution and number of parameters without overfitting, generalizing better on out-of-domain data such as the NuScenes dataset. Furthermore, it does not require large-scale supervised pretraining on ImageNet and can run in real-time. Finally, we release DDAD (Dense Depth for Automated Driving), a new urban driving dataset with more challenging and accurate depth evaluation, thanks to longer-range and denser ground-truth depth generated from high-density LiDARs mounted on a fleet of self-driving cars operating world-wide.

• The paper presents PackNet, a neural architecture that uses 3D convolutions to preserve high-resolution spatial details during encoding and decoding.
• The method leverages a self-supervised learning paradigm, integrating geometric principles to achieve scale-aware and metrically accurate depth predictions.
• Performance is validated on the KITTI benchmark and the new DDAD dataset, demonstrating superior results in complex, extended-depth scenarios.

Analysis of 3D Packing for Self-Supervised Monocular Depth Estimation

This paper presents a sophisticated approach to monocular depth estimation using a novel self-supervised methodology, which relies on a newly developed neural architecture named PackNet. The authors propose an innovative mechanism of symmetrical packing and unpacking blocks leveraging 3D convolutions to learn detailed and accurate depth representations from monocular videos without requiring labeled data.

Key Contributions

1. PackNet Architecture: The primary contribution is the introduction of PackNet, which uses 3D convolutions to preserve intricate spatial details during both encoding and decoding processes. This approach contrasts with traditional architectures that often rely on aggressive pooling techniques leading to loss of important information. PackNet is designed to excel at maintaining high-resolution details, which is crucial for depth estimation tasks.
2. Self-Supervised Learning Paradigm: The proposed solution strategically integrates geometry with deep learning. By using only image sequences, the method circumvents the need for ground-truth depth supervision, thus making it broadly applicable to various datasets and environments.
3. Performance on Benchmarks: The paper substantiates the efficacy of PackNet on the KITTI benchmark, demonstrating superiority over existing self and fully supervised methods, particularly at extended depth ranges. This highlights the model's robustness in preserving details even in complex task settings.
4. Scale-Aware Depth Estimation: The authors address an intrinsic limitation of scale ambiguity in monocular vision by introducing a novel loss function that incorporates the camera's velocity, making depth predictions metrically accurate and not reliant on LiDAR ground-truth scaling at test time.
5. DDAD Dataset: Another significant contribution is the release of a novel dataset, Dense Depth for Automated Driving (DDAD), featuring high-resolution and long-range depth information. This dataset aids in evaluating and benchmarking monocular depth estimation models at longer ranges, presenting challenges that more accurately reflect real-world scenarios.

Strong Numerical Results

PackNet achieves strong results, with significant improvements over the state-of-the-art methods across several metrics on the KITTI dataset. For instance, using a 1280 x 384 resolution, PackNet achieves an absolute relative error of 0.104 and scales better with additional data, affirming its generalization capability.

Implications and Future Directions

The theoretical implication centers on the potential for neural network architectures to maintain fine-grained details necessary for accurate perception tasks, independent of massive labeled datasets. Practically, the proposed method could revolutionize applications in robotics and autonomous driving, where real-world visual perception is critical.

Future research could focus on extending PackNet's capabilities by integrating multi-modal inputs or exploring more efficient model variants for deployment in resource-constrained environments. Further evaluation on other datasets can also substantiate its generalizability across diverse settings.

In conclusion, this paper makes substantial advancements in the field of self-supervised depth estimation, providing a robust architecture that balances performance and generalizability, potentially setting a new standard for future research in depth estimation.