Panoptic Free Download
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In panoptic segmentation, an instance can either represent a distinct thing or a region of stuff. Things are countable objects such as pedestrians, animals, or cars, while stuff represents uncountable amorphous regions such as the sky or grass. For example, in this image, the different cars are labeled as different things and are thus separate instances. Meanwhile, the road is seen as stuff and is thus labeled as a single instance.
COCO is a large dataset of common objects in their context. It features over 200K labeled images of objects such as different kinds of animals, appliances, food, and much more. The panoptic task uses 80 thing categories as well as several stuff categories.
SemanticKITTI is a dataset of lidar sequences of street scenes in Karlsruhe (Germany). It contains 11 driving sequences with panoptic segmentation labels. The labels use 6 thing and 16 stuff categories.
ScanNet is an RGB-D video dataset of indoor scenes containing 2.5 million views in 1513 scans. It uses 38 thing categories for items and furniture in the rooms and 2 stuff categories (wall and floor). It is not a complete panoptic dataset, as the labels only cover about 90% of all surfaces.
Finally, you have to label your selected data. For panoptic segmentation, this means creating segmentation masks for each instance and each background region. This can be a tedious and time-consuming process, but with the right tools you can speed up your labeling significantly.
There are a number of public datasets for panoptic datasets. Most of them consist of urban driving imagery and are thus suited for autonomous vehicle applications. There are also datasets for common everyday objects.
Our dataset is based on the KITTI Vision Benchmark and therefore we distribute the data under Creative Commons Attribution-NonCommercial-ShareAlike license. You are free to share and adapt the data, but have to give appropriate credit and may not use the work for commercial purposes.
We provide the voxel grids for learning and inference, which you must download to get the SemanticKITTI voxel data (700 MB). This archive contains the training (all files) and test data (only bin files). Refer to the development kit to see how to read our binary files.
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This demo shows the panoptic segmentation performance of our EfficeintPS model trained on four challenging urban scene understanding datasets. EfficientPS is currently ranked #1 for panoptic segmentation on standard benchmark datasets such as Cityscapes, KITTI, Mapillary Vistas, and IDD. Additionally, EfficientPS is also ranked #2 on the Cityscapes semantic segmentation benchmark as well as #2 on the Cityscapes instance segmentation benchmark, among the published methods. To learn more about panoptic segmentation and the approach employed, please see the Technical Approach. View the demo by selecting a dataset to load from the drop down box below and click on an image in the carosel to see live results. The results are shown as an overlay of panoptic segmentation over the input image. The colors of the overlay denote what semantic category that the pixel belongs to and the instances of objects are indicated with a white boundary.
A relatively new approach to scene understanding called as panoptic segmentation aims to use a single convolutional neural network to simultaneously recognize distinct foreground objects such as people, cyclists or cars (a task called instance segmentation), while also labeling pixels in the image background with classes such as road, sky, or grass (a task called semantic segmentation). Most early research has primarily explored these two segmentation tasks separately using different types of network architectures. However, this disjoint approach has several drawbacks including large computational overhead, redundancy in learning and discrepancy between the predictions of each network. Although recent methods have made significant strides to address this task in top-down manner with shared components or in a bottom-up manner sequentially, these approaches still face several challenges in terms of computational efficiency, slow runtimes and subpar results compared to task-specific state-of-the-art networks. To address these issues, we study the typical design choices for such networks and make several key advances that we incorporate in our EfficientPS architecture, which improves both performance as well as efficiency.
The idea behind EfficientPS is influenced by our goal of achieving superior performance compared to prior state-of-the-art models while simultaneously being fast and computationally efficient. Initial panoptic segmentation methods heuristically combine predictions from state-of-the-art instance segmentation network and semantic segmentation network in a post-processing step. However, this disjoint approach has several drawbacks including large computational overhead, redundancy in learning and discrepancy between the predictions of each network. Although recent methods have made significant strides to address this task in top-down manner with shared components or in a bottom-up manner sequentially, these approaches still face several challenges in terms of computational efficiency, slow runtimes and subpar results compared to task-specific individual networks.
We address the aforementioned challenges in our EfficientPS architecture that provides effective solutions to these problems. EfficientPS consists of our new shared backbone with mobile inverted bottleneck units and our proposed 2-way Feature Pyramid Network (FPN), followed by task-specific instance and semantic segmentation heads with seperable convolutions, whose outputs are combined in our parameter-free panoptic fusion module. The entire network is jointly optimized in an end-to-end manner to yield the final panoptic segmentation output.
Previous panoptic segmentation architectures rely on ResNets or ResNeXts with Feature Pyramid Network (FPN) as the backbone, which consume a significant amount of parameters and have a limited representational capacity. In order to achieve a better efficiency, we propose a new backbone network consisting of a modified EfficientNet that employs compound scaling to uniformly scale all the dimensions of the network, coupled with our novel 2-way FPN. We identify that the standard FPN has its limitations to aggregate multi-scale features due to the unidirectional flow of information. Therefore, we introduce the novel 2-way FPN that facilities bidirectional flow of information which substantially improves the panoptic quality of foreground classes while remaining comparable in runtime.
We incorporate our proposed semantic head with separable convolutions that captures fine features andlong-range context efficiently as well as correlates them before fusion for better object boundary refinement. We build upon Mask R-CNN for the instance head and augment it with separable convolutions and iABN sync layers. One of the critical challenges in panoptic segmentation deals with resolving the conflict of overlapping predictions from the semantic and instance heads. In order to thoroughly exploit the logits from both heads, we propose a new panoptic fusion module that dynamically adapts the fusion of logits from the semantic and instance heads based on their mask confidences and congruously integrates instance-specific foreground classes with background classes to yield the final panoptic segmentation output.
We evaluate EfficientPS on four challenging urban scene understanding benchmark datasets, namely Cityscapes, Mapillary Vistas, KITTI and IDD. EfficientPS is ranked #1 for panoptic segmentation on the widely used Cityscapes benchmark leaderboard, exceeding the prior state-of-the-art model by a large margin, while consuming fewer parameters, lesser computation and faster inference time. In addition, EfficientPS is also ranked #2 on the Cityscapes semantic segmentation benchmark as well as the Cityscapes instance segmentation benchmark, among the published methods. EfficientPS consistently achieves state-of-the-art panoptic segmentation performance on Mapillary Vistas, KITTI and IDD benchmark datasets. EfficientPS is the first to benchmark on all the four standard urban scene understanding datasets for panoptic segmentation and exceed the state-of-the-art on each of them while simultaneously being the most efficient model. Achieving computationally efficient rich and coherent image segmentation has widespread implications for image recognition systems that have to make sense of cluttered real-world environments, where objects move and overlap. Segmenting foreground objects together with background is important for understanding entire scenes and for performing related actions, such as navigating through dynamic scenes.
Given the exceptional performance of our EfficentPS, we expect it could serve as a new foundation of future segmentation related researc
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