PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds (1911.12408v2)

Abstract: We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume, upsampling, and warping layers to efficiently handle 3D point cloud data. Unlike traditional cost volumes that require exhaustively computing all the cost values on a high-dimensional grid, our point-based formulation discretizes the cost volume onto input 3D points, and a PointConv operation efficiently computes convolutions on the cost volume. Experiment results on FlyingThings3D outperform the state-of-the-art by a large margin. We further explore novel self-supervised losses to train our model and achieve comparable results to state-of-the-art trained with supervised loss. Without any fine-tuning, our method also shows great generalization ability on KITTI Scene Flow 2015 dataset, outperforming all previous methods.

• The paper introduces PointPWC-Net, which innovatively combines a coarse-to-fine strategy with self-supervised learning for accurate 3D scene flow estimation.
• The model computes a learnable cost volume directly on 3D points using PointConv and a feature pyramid, significantly boosting computational efficiency.
• Experiments on FlyingThings3D and KITTI datasets demonstrate state-of-the-art performance in handling large motions and non-rigid dynamics.

PointPWC-Net: Advanced Scene Flow Estimation for 3D Point Clouds

The paper introduces PointPWC-Net, an innovative model for estimating scene flow directly from 3D point clouds by leveraging a novel approach to cost volume computation and integrating self-supervised learning techniques. This work stands out for its focus on large motions and non-rigid environments, which are critical in applications such as autonomous driving where 3D sensors like LiDAR are frequently used.

Model Overview

PointPWC-Net employs a coarse-to-fine strategy, enabling efficient scene flow estimation even with substantial motion between point cloud frames. The model's architecture incorporates:

1. Learnable Cost Volume Layer: By discretizing the cost volume onto the input 3D points rather than a regular grid, PointPWC-Net achieves computational efficiency and scalability. This approach surpasses traditional methods that rely on dense 4D tensors by employing the PointConv operation to compute convolutions directly on the irregularly sampled cost volume.
2. Feature Pyramid: The model builds a multi-level pyramid from 3D point clouds, enhancing feature capture over varying spatial scales. This hierarchical processing framework supports detailed and context-aware scene flow predictions.
3. Warping and Upsampling Layers: To handle the dynamics of 3D environments, PointPWC-Net includes warping layers which adapt coarse flow predictions to finer levels, thereby reducing search space dimensionality, and upsampling layers that propagate flow estimations across the pyramid.
4. Self-supervised Losses: Introducing novel constraints such as Chamfer distance, smoothness, and Laplacian regularization, the self-supervised paradigm within PointPWC-Net circumvents the need for labeled data typically required for training, yet still delivers competitive results.

Experimental Results

Extensive experiments underscore the superiority of PointPWC-Net. On the synthetic FlyingThings3D dataset, the model significantly outperforms existing state-of-the-art methods across multiple metrics, including EPE3D and Acc3DS. Moreover, the model demonstrates remarkable generalization capabilities when evaluated on the real-world KITTI Scene Flow 2015 dataset, even without any task-specific fine-tuning. This robustness is further emphasized by achieving state-of-the-art performance when the model is fine-tuned using self-supervised techniques.

Implications and Future Directions

The contributions set forth by PointPWC-Net have several practical and theoretical implications:

• Computational Efficiency: By mitigating the computational load associated with traditional 3D cost volume computation, this approach facilitates real-time applications in environments with limited computational resources.
• Self-supervised Learning: The introduction of self-supervision in scene flow estimation aligns with the growing trend of reducing reliance on large annotated datasets, which are often costly to acquire.
• Potential Applications: The model promises significant advancements in autonomous systems, virtual reality, and robotics, where understanding 3D motion dynamics seamlessly is crucial.

Prospective research could explore more sophisticated self-supervised loss functions that capture even finer details of point cloud dynamics. Additionally, adapting the network to efficiently handle other forms of sensor data, such as RGB-D, could further broaden its applicability. The fusion of multi-modal data streams could notably enhance the precision and robustness of scene flow estimations across diverse real-world conditions.

PointPWC-Net exemplifies substantial progress in 3D motion estimation, setting a new benchmark for the integration of deep learning techniques into the domain of point cloud analysis.