EC-Depth: Exploring the consistency of self-supervised monocular depth estimation under challenging scenes

Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically designed to train and test on clear and pristine datasets, overlooking the impact of various adverse conditions prevalent in real-world scenarios. As a result, it is commonly observed that most self-supervised monocular depth estimation methods struggle to perform adequately under challenging conditions. To address this issue, we present EC-Depth, a novel self-supervised two-stage training framework to achieve a robust depth estimation, starting from the foundation of depth prediction consistency under different perturbations. Leveraging the proposed perturbation-invariant depth consistency constraint module and the consistency-based pseudo-label selection module, our model attains accurate and consistent depth predictions in both standard and challenging scenarios. Extensive experiments substantiate the effectiveness of the proposed method. Moreover, our method surpasses existing state-of-the-art methods on KITTI, KITTI-C and DrivingStereo benchmarks, demonstrating its potential for enhancing the reliability of self-supervised monocular depth estimation models in real-world applications.

SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence

Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and various orientations, which lead to severe mispredictions of symmetrical parts. Besides, point cloud noise disrupts consistent representations for point cloud and thus degrades the shape correspondence accuracy. To address the above issues, we propose a Self-Ensembling ORientation-aware Network termed SE-ORNet. The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts. Additionally, we design a selfensembling framework for unsupervised point cloud shape correspondence. In this framework, the disturbances of point cloud noise are overcome by perturbing the inputs of the student and teacher networks with different data augmentations and constraining the consistency of predictions. Extensive experiments on both human and animal datasets show that our SE-ORNet can surpass state-of-the-art unsupervised point cloud shape correspondence methods.

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used by vanilla foldings limits its potential of generating a complex and faithful shape. Enlightened by the success of StyleGAN, we regard the shape feature as style code that modulates the normalization layers during the folding, which considerably enhances its capability. Thirdly, we realize that existing point supervisions, e.g., Chamfer Distance or Earth Mover's Distance, cannot faithfully reflect the perceptual quality of the reconstructed points. To address this, we propose to project the completed points to depth maps with a differentiable renderer and apply adversarial training to advocate the perceptual realism under different viewpoints. Comprehensive experiments on ShapeNet and KITTI prove the effectiveness of our method, which achieves state-of-the-art quantitative performance while offering superior visual quality. Code is available at https://github.com/microsoft/SpareNet.