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3D Instance Segmentation via Multi-Task Metric Learning
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TLDR
This work proposes a novel method for instance label segmentation of dense 3D voxel grids that achieves state-of-the-art performance on the ScanNet 3D instance segmentation benchmark.Abstract:
We propose a novel method for instance label segmentation of dense 3D voxel grids. We target volumetric scene representations, which have been acquired with depth sensors or multi-view stereo methods and which have been processed with semantic 3D reconstruction or scene completion methods. The main task is to learn shape information about individual object instances in order to accurately separate them, including connected and incompletely scanned objects. We solve the 3D instance-labeling problem with a multi-task learning strategy. The first goal is to learn an abstract feature embedding, which groups voxels with the same instance label close to each other while separating clusters with different instance labels from each other. The second goal is to learn instance information by densely estimating directional information of the instance's center of mass for each voxel. This is particularly useful to find instance boundaries in the clustering post-processing step, as well as, for scoring the segmentation quality for the first goal. Both synthetic and real-world experiments demonstrate the viability and merits of our approach. In fact, it achieves state-of-the-art performance on the ScanNet 3D instance segmentation benchmark.read more
Citations
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Journal ArticleDOI
Deep Learning for 3D Point Clouds: A Survey
TL;DR: This paper presents a comprehensive review of recent progress in deep learning methods for point clouds, covering three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
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From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network
TL;DR: This paper extends the preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network, which outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D objects detection dataset by utilizing only the LiDAR point cloud data.
Book ChapterDOI
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
TL;DR: In this paper, the authors propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch.
Proceedings ArticleDOI
3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation
TL;DR: It is shown that grouping proposals improves over NMS and outperforms previous state-of-the-art methods on the tasks of 3D object detection and semantic instance segmentation on the ScanNetV2 benchmark and the S3DIS dataset.
Journal ArticleDOI
Review: deep learning on 3D point clouds
TL;DR: A survey of the recent state-of-the-art deep learning techniques that mainly focused on point cloud data, introducing the popular 3D point cloud benchmark datasets, and discussing the application of deep learning in popular3D vision tasks including classification, segmentation and detection.
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