Lightweight integration of 3D features to improve 2D image segmentation

• URL: http://arxiv.org/abs/2212.08334v2
• Date: Mon, 10 Jul 2023 08:38:08 GMT
• Title: Lightweight integration of 3D features to improve 2D image segmentation
• Authors: Olivier Pradelle and Raphaelle Chaine and David Wendland and Julie Digne
• Abstract summary: We show that image segmentation can benefit from 3D geometric information without requiring a 3D groundtruth. Our method can be applied to many 2D segmentation networks, improving significantly their performance.
• Score: 1.3799488979862027
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Scene understanding has made tremendous progress over the past few years, as data acquisition systems are now providing an increasing amount of data of various modalities (point cloud, depth, RGB...). However, this improvement comes at a large cost on computation resources and data annotation requirements. To analyze geometric information and images jointly, many approaches rely on both a 2D loss and 3D loss, requiring not only 2D per pixel-labels but also 3D per-point labels. However, obtaining a 3D groundtruth is challenging, time-consuming and error-prone. In this paper, we show that image segmentation can benefit from 3D geometric information without requiring a 3D groundtruth, by training the geometric feature extraction and the 2D segmentation network jointly, in an end-to-end fashion, using only the 2D segmentation loss. Our method starts by extracting a map of 3D features directly from a provided point cloud by using a lightweight 3D neural network. The 3D feature map, merged with the RGB image, is then used as an input to a classical image segmentation network. Our method can be applied to many 2D segmentation networks, improving significantly their performance with only a marginal network weight increase and light input dataset requirements, since no 3D groundtruth is required.

Related papers

• SAM-guided Graph Cut for 3D Instance Segmentation [60.75119991853605]
  This paper addresses the challenge of 3D instance segmentation by simultaneously leveraging 3D geometric and multi-view image information. We introduce a novel 3D-to-2D query framework to effectively exploit 2D segmentation models for 3D instance segmentation. Our method achieves robust segmentation performance and can generalize across different types of scenes.
  arXiv  Detail & Related papers  (2023-12-13T18:59:58Z)
• DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields [68.94868475824575]
  This paper introduces a novel approach capable of generating infinite, high-quality 3D-consistent 2D annotations alongside 3D point cloud segmentations. We leverage the strong semantic prior within a 3D generative model to train a semantic decoder. Once trained, the decoder efficiently generalizes across the latent space, enabling the generation of infinite data.
  arXiv  Detail & Related papers  (2023-11-18T21:58:28Z)
• Multi-View Representation is What You Need for Point-Cloud Pre-Training [22.55455166875263]
  This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks. We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks.
  arXiv  Detail & Related papers  (2023-06-05T03:14:54Z)
• SSR-2D: Semantic 3D Scene Reconstruction from 2D Images [54.46126685716471]
  In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images. Our method achieves the state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet.
  arXiv  Detail & Related papers  (2023-02-07T17:47:52Z)
• Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation [3.5939555573102853]
  Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network. We propose an end-to-end trainable multi-view aggregation model leveraging the viewing conditions of 3D points to merge features from images taken at arbitrary positions. Our method can combine standard 2D and 3D networks and outperforms both 3D models operating on colorized point clouds and hybrid 2D/3D networks.
  arXiv  Detail & Related papers  (2022-04-15T17:10:48Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• Multi-Modality Task Cascade for 3D Object Detection [22.131228757850373]
  Many methods train two models in isolation and use simple feature concatenation to represent 3D sensor data. We propose a novel Multi-Modality Task Cascade network (MTC-RCNN) that leverages 3D box proposals to improve 2D segmentation predictions. We show that including a 2D network between two stages of 3D modules significantly improves both 2D and 3D task performance.
  arXiv  Detail & Related papers  (2021-07-08T17:55:01Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)
• Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation [18.76436457395804]
  Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achieving clinically applicable image segmentation performance on CT or MRI images. We propose a new framework for combining 3D and 2D models, in which the segmentation is realized through high-resolution 2D convolutions.
  arXiv  Detail & Related papers  (2020-12-16T21:39:53Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.