This paper investigates how to leverage more readily acquired annotations, i.e., 3D bounding boxes instead of dense point-wise labels, for instance segmentation. We propose a Weakly-supervised point cloud Instance Segmentation framework with Geometric Priors (WISGP) that allows segmentation models to be trained with 3D bounding boxes of instances. Considering intersections among bounding boxes in a scene would result in ambiguous la- bels, we first group points into two sets, i.e., univocal and equivocal sets, indicating the certainty of a 3D point belonging to an instance, respectively. Specifically, 3D points with clear labels belong to the univocal set while the rest are grouped into the equivocal set. To assign reliable labels to points in the equivocal set, we design a Geometry-guided Label Propagation (GLP) scheme that progressively propagates labels to linked points based on geometric structure, e.g., polygon meshes and superpoints. Afterwards, we train an instance segmentation model with the univocal points and equivocal points labeled by GLP, and then employ it to assign pseudo labels for the remainder of the unlabeled points. Lastly, we retrain the model with all the labeled points to achieve better instance segmentation performance. Experiments on large-scale datasets ScanNet-v2 and S3DIS demonstrate that WISGP is superior to competing weakly-supervised algorithms and even on par with a few fully-supervised ones.

We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying YOCO improves the diversity of the augmentation per sample and encourages neural networks to recognize objects from partial information. YOCO enjoys the properties of parameter-free, easy usage, and boosting almost all augmentations for free. Thorough experiments are conducted to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly applied to varying data augmentations, neural network architectures, and brings performance gains on CIFAR and ImageNet classification tasks, sometimes surpassing conventional image-level augmentation by large margins. Moreover, we show YOCO benefits contrastive pre-training toward a more powerful representation that can be better transferred to multiple downstream tasks. Finally, we study a number of variants of YOCO and empirically analyze the performance for respective settings.

Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by leveraging from contrastive learning and generative adversarial networks to maximize mutual information between raw and restored images. Additionally, we release a large-scale real underwater image dataset to support both paired and unpaired training modules. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method.

This paper introduces a novel method to simultaneously super-resolve and colour-predict images acquired by snapshot mosaic sensors. These sensors allow for spectral images to be acquired using low-power, small form factor, solid-state CMOS sensors that can operate at video frame rates without the need for complex optical setups. Despite their desirable traits, their main drawback stems from the fact that the spatial resolution of the imagery acquired by these sensors is low. Moreover, chromatic mapping in snapshot mosaic sensors is not straightforward since the bands delivered by the sensor tend to be narrow and unevenly distributed across the range in which they operate. We tackle this drawback as applied to chromatic mapping by using a residual channel attention network equipped with a texture sensitive block. Our method significantly outperforms the traditional approach of interpolating the image and, afterwards, applying a colour matching function. This work establishes state-of-the-art in this domain while also making available to the research community a dataset containing 296 registered stereo multi-spectral/RGB images pairs.