Deep Watershed Transform for Instance Segmentation
Min Bai,Raquel Urtasun +1 more
- pp 2858-2866
TLDR
This paper presents a simple yet powerful end-to-end convolutional neural network that achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.Abstract:
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In this paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. Our approach combines intuitions from the classical watershed transform and modern deep learning to produce an energy map of the image where object instances are unambiguously represented as energy basins. We then perform a cut at a single energy level to directly yield connected components corresponding to object instances. Our model achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.read more
Citations
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Real-time Progressive 3D Semantic Segmentation for Indoor Scene
TL;DR: In this article, the authors proposed an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. But their method is built atop an efficient super-voxel clustering method and a conditional random field with higher-order constraints from structural and object cues.
Proceedings ArticleDOI
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez,Sasha Doubov,Jack Fan,loan Andrei Barsan,Shenlong Wang,Gellert Mattyus,Raquel Urtasun +6 more
TL;DR: The Pit30M dataset as discussed by the authors is a large-scale image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous work.
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Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation
TL;DR: MaskProp as mentioned in this paper proposes a mask propagation branch to propagate frame-level instance masks from each video frame to all the other frames in a video clip, which allows the system to predict cliplevel instance tracks with respect to the object instances segmented in the middle frame of the clip.
Proceedings ArticleDOI
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
TL;DR: CMT-DeepLab as discussed by the authors proposes a transformer-based framework for panoptic segmentation designed around clustering, which considers the object queries as cluster centers, which fill the role of grouping the pixels when applied to segmentation.
Proceedings ArticleDOI
The Task of Instance Segmentation of Mineral Grains in Digital Images of Rock Samples (Thin Sections)
TL;DR: The paper considers developing the method for instance segmentation of mineral grains in thin section images of sandstone, and proposes a flexible and robust solution based on a cascade of two fully-convolution neural networks (FCNN).
References
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Proceedings ArticleDOI
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