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检索条件"主题词=3D semantic segmentation"

共 98 条记录，以下是11-20 订阅

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Language-Grounded Indoor 3d semantic segmentation in the Wild 1

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17th European Conference on Computer Vision (ECCV)

作者： Rozenberszki, david Litany, Or dai, Angela Tech Univ Munich Munich Germany NVIDIA Santa Clara CA USA

ISBN: (数字)9783031198274

ISBN: (纸本)9783031198267;9783031198274

Recent advances in 3d semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets. However, current 3d semantic segmentation benchmarks contain only a small number of categories - less than 30 for ScanNet and semanticKITTI, for instance, which are not enough to reflect the diversity of real environments (e.g., semantic image understanding covers hundreds to thousands of classes). Thus, we propose to study a larger vocabulary for 3d semantic segmentation with a new extended benchmark on ScanNet data with 200 class categories, an order of magnitude more than previously studied. This large number of class categories also induces a large natural class imbalance, both of which are challenging for existing 3d semantic segmentation methods. To learn more robust 3d features in this context, we propose a language-driven pre-training method to encourage learned 3d features that might have limited training examples to lie close to their pre-trained text embeddings. Extensive experiments show that our approach consistently outperforms state-of-the-art 3d pre-training for 3d semantic segmentation on our proposed benchmark (+9% relative mIoU), including limited-data scenarios with +25% relative mIoU using only 5% annotations.

关键词： 3d semantic scene understanding 3d semantic segmentation 3d representation learning Language+3d vision

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dOdA: data-Oriented Sim-to-Real domain Adaptation for 3d semantic segmentation 1

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17th European Conference on Computer Vision (ECCV)

作者： ding, Runyu Yang, Jihan Jiang, Li Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China MPI Informat Saarbrucken Germany

ISBN: (数字)9783031198120

ISBN: (纸本)9783031198113;9783031198120

deep learning approaches achieve prominent success in 3d semantic segmentation. However, collecting densely annotated real-world 3d datasets is extremely time-consuming and expensive. Training models on synthetic data and generalizing on real-world scenarios becomes an appealing alternative, but unfortunately suffers from notorious domain shifts. In this work, we propose a data-Oriented domain Adaptation (dOdA) framework to mitigate pattern and context gaps caused by different sensing mechanisms and layout placements across domains. Our dOdA encompasses virtual scan simulation to imitate real-world point cloud patterns and tail-aware cuboid mixing to alleviate the interior context gap with a cuboid-based intermediate domain. The first unsupervised sim-to-real adaptation benchmark on 3d indoor semantic segmentation is also built on 3d-FRONT, ScanNet and S3dIS along with 8 popular Unsupervised domain Adaptation (UdA) methods. Our dOdA surpasses existing UdA approaches by over 13% on both 3d-FRONT -> ScanNet and 3d-FRONT -> S3dIS. Code is available at https://***/CVMI-Lab/dOdA.

关键词： domain adaptation 3d semantic segmentation

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Cross-domain and Cross-Modal Knowledge distillation in domain Adaptation for 3d semantic segmentation 22

Cross-Domain and Cross-Modal Knowledge Distillation in Domai...

引用

30th ACM International Conference on Multimedia (MM)

作者： Li, Miaoyu Zhang, Yachao Xie, Yuan Gao, Zuodong Li, Cuihua Zhang, Zhizhong Qu, Yanyun Xiamen Univ Xiamen Peoples R China East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781450392037

With the emergence of multi-modal datasets where LidAR and camera are synchronized and calibrated, cross-modal Unsupervised domain Adaptation (UdA) has attracted increasing attention because it reduces the laborious annotation of target domain samples. To alleviate the distribution gap between source and target domains, existing methods conduct feature alignment by using adversarial learning. However, it is well-known to be highly sensitive to hyperparameters and difficult to train. In this paper, we propose a novel model (dual-Cross) that integrates Cross-domain Knowledge distillation (CdKd) and Cross-Modal Knowledge distillation (CMKd) to mitigate domain shift. Specifically, we design the multi-modal style transfer to convert source image and point cloud to target style. With these synthetic samples as input, we introduce a target-aware teacher network to learn knowledge of the target domain. Then we present dual-cross knowledge distillation when the student is learning on source domain. CdKd constrains teacher and student predictions under same modality to be consistent. It can transfer target-aware knowledge from the teacher to the student, making the student more adaptive to the target domain. CMKd generates hybrid-modal prediction from the teacher predictions and constrains it to be consistent with both 2d and 3d student predictions. It promotes the information interaction between two modalities to make them complement each other. From the evaluation results on various domain adaptation settings, dual-Cross significantly outperforms both uni-modal and cross-modal state-of-the-art methods.

关键词： Unsupervised domain adaptation Target-aware teacher network Cross-domain knowledge distillation 3d semantic segmentation Multi-modal style transfer

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Learning 3d semantic segmentation with only 2d Image Supervision 9

Learning 3D Semantic Segmentation with only 2D Image Supervi...

引用

9th International Conference on 3d Vision (3dV)

作者： Genova, Kyle Yin, Xiaoqi Kundu, Abhijit Pantofaru, Caroline Cole, Forrester Sud, Avneesh Brewington, Brian Shucker, Brian Funkhouser, Thomas Google Res Mountain View CA 94043 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665426886

With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3d data collected from terrestrial platforms with lidar scanners and color cameras. However, due to high labeling costs, ground-truth 3d semantic segmentation annotations are limited in both quantity and geographic diversity, while also being difficult to transfer across sensors. In contrast, large image collections with ground-truth semantic segmentations are readily available for diverse sets of scenes. In this paper, we investigate how to use only those labeled 2d image collections to supervise training 3d semantic segmentation models. Our approach is to train a 3d model from pseudo-labels derived from 2d semantic image segmentations using multiview fusion. We address several novel issues with this approach, including how to select trusted pseudo-labels, how to sample 3d scenes with rare object categories, and how to decouple input features from 2d images from pseudo-labels during training. The proposed network architecture, 2d3dNet, achieves significantly better performance (+6.2-11.4 mIoU) than baselines during experiments on a new urban dataset with lidar and images captured in 20 cities across 5 continents.

关键词： 2d3dNet 3d datasets 3d semantic segmentation Cross modal Supervision Multi view Fusion Sparse Voxel Convolution

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Cross-modal Unsupervised domain Adaptation for 3d semantic segmentation via Bidirectional Fusion-then-distillation 23

Cross-modal Unsupervised Domain Adaptation for 3D Semantic S...

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31st ACM International Conference on Multimedia (MM)

作者： Wu, Yao Xing, Mingwei Zhang, Yachao Xie, Yuan Fan, Jianping Shi, Zhongchao Qu, Yanyun Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China Tsinghua Univ Shenzhen Peoples R China East China Normal Univ Shanghai Peoples R China East China Normal Univ Chongqing Inst Chongqing Peoples R China Lenovo Res Beijing Peoples R China

ISBN: (纸本)9798400701085

Cross-modal Unsupervised domain Adaptation (UdA) becomes a research hotspot because it reduces the laborious annotation of target domain samples. Existing methods only mutually mimic the outputs of cross-modality in each domain, which enforces the class probability distribution agreeable in different domains. However, these methods ignore the complementarity brought by the modality fusion representation in cross-modal learning. In this paper, we propose a cross-modal UdA method for 3d semantic segmentation via Bidirectional Fusion-then-distillation, named BFtd-xMUdA, which explores cross-modal fusion in UdA and realizes distribution consistency between outputs of two domains not only for 2d image and 3d point cloud but also for 2d/3d and fusion. Our method contains three significant components: Model-agnostic Feature Fusion Module (MFFM), Bidirectional distillation (B-distill), and Cross-modal debiased Pseudo-Labeling (xdPL). MFFM is employed to generate cross-modal fusion features for establishing a latent space, which enforces maximum correlation and complementarity between two heterogeneous modalities. B-distill is introduced to exploit bidirectional knowledge distillation which includes cross-modality and cross-domain fusion distillation, and well-achieving domain-modality alignment. xdPL is designed to model the uncertainty of pseudo-labels by self-training scheme. Extensive experimental results demonstrate that our method outperforms state-of-the-art competitors in several adaptation scenarios.

关键词： 3d semantic segmentation Unsupervised domain adaptation

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Open-Vocabulary 3d semantic segmentation with Foundation Models

Open-Vocabulary 3D Semantic Segmentation with Foundation Mod...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiang, Li Shi, Shaoshuai Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (纸本)9798350353006

In dynamic 3d environments, the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need, we introduce OV3d, an innovative framework designed for open-vocabulary 3d semantic segmentation. OV3d leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3d points and textual entity descriptions. These entity descriptions are enriched with contextual information, enabling a more open and comprehensive understanding. By seamlessly aligning 3d point features with entity text features, OV3d empow-ers open-vocabulary recognition in the 3d domain, achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets, including ScanNet, Matterport3d, and nuScenes.

关键词： 3d semantic segmentation Foundation Models Open Vocabulary

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Iterative deep Fusion for 3d semantic segmentation 4

Iterative Deep Fusion for 3D Semantic Segmentation

引用

4th IEEE International Conference on Robotic Computing (IEEE IRC)

作者： duerr, Fabian Weigel, Hendrik Maehlisch, Mirko Beyerer, Juergen AUDI AG Dept Sensor Fus & MapLearning Automated Driving Ingolstadt Germany Fraunhofer Ctr Machine Learning Fraunhofer Inst Optron Syst Technol & Image Exploitat IOSB Karlsruhe Germany Karlsruhe Inst Technol Vis & Fus Lab KIT Karlsruhe Germany

ISBN: (纸本)9781728152370

Understanding and interpreting a scene is a key task of environment perception for autonomous driving, which is why autonomous vehicles are equipped with a wide range of different sensors. semantic segmentation of sensor data provides valuable information for this task and is often seen as key enabler. In this paper, we are presenting a deep learning approach for 3d semantic segmentation of lidar point clouds. The proposed architecture uses a range view representation of 3d point clouds and additionally exploits camera features to increase accuracy and robustness. In contrast to other approaches, which fuse lidar and camera feature maps once, we fuse them iteratively and at different scales inside our network architecture. We demonstrate the benefits of the presented iterative deep fusion approach over single fusion approaches on a large benchmark dataset. Our evaluation shows considerable improvements, resulting from the additional use of camera features. Furthermore, our fusion strategy outperforms the current state-of-the-art strategy by a considerable margin. despite the use of camera features, the presented approach is also trainable solely with point cloud labels.

关键词： 3d semantic segmentation Convolutional Neural Network deep Sensor Fusion Lidar

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CLFusion:3d semantic segmentation Based on Camera and Lidar Fusion

CLFusion:3D Semantic Segmentation Based on Camera and Lidar ...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Wang, Tianyue Song, Rujun Xiao, Zhuoling Yan, Bo Qin, Haojie He, di Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9798350330991;9798350331004

In the field of autonomous driving, semantic segmentation is crucial for scene understanding. Currently, there are two main methods: camera-based and Lidar-based approaches. To address the issues of Lidar segmentation lacking texture features and image segmentation lacking distance information, this paper proposes a fusion of camera and Lidar to achieve 3d semantic segmentation. The method utilizes a dual-stream encoder-decoder network to process camera images and Lidar point cloud and incorporates a specially designed attention mechanism module for feature fusion. To avoid expensive manual annotation of 3d point clouds, the study also introduces a cross-dataset and cross-modal self-supervised training approach. Experimental results show a 2.4% improvement compared to the Lidar-only mode baseline results on the semanticKITTI dataset and a 6% improvement on the nuScenes dataset.

关键词： 3d semantic segmentation multi-modal fusion self-supervised training

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ROBUST 3d semantic segmentation WITH INCOMPLETE POINT CLOUdS BASEd ON SEQUENTIAL FRAME SAMPLING 31

ROBUST 3D SEMANTIC SEGMENTATION WITH INCOMPLETE POINT CLOUDS...

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2024 International Conference on Image Processing

作者： Yamaguchi, Masahiro Higa, Kyota Hosoi, Toshinori Shibata, Takashi NEC Corp Ltd Visual Intelligence Res Labs Shimonumabe 1753Nakahara Ku Kawasaki Kanagawa Japan

ISBN: (纸本)9798350349405;9798350349399

This paper proposes a method for learning 3d semantic segmentation robust to incomplete point clouds. Our method first generates pseud-incomplete point clouds from original 3d point clouds by sequential frame sampling that creates multiple subsets considering the continuity of an RGB-d sequence for reproducing incomplete areas in the point clouds. It then simultaneously learns completion networks and semantic segmentation networks with the pseud-incomplete point clouds. We evaluate our method on the 3d semantic segmentation task. Experimental results on ScanNet v2, an indoor environment, show that our method improves mIoU by 0.4 points for the original point clouds and 6.3 points for the incomplete point clouds compared with a conventional method. Experimental results on WorkPlace dataset, an outdoor environment, show that our method improves mIoU by 6.5 points for the original point clouds and 11.1 points for the incomplete point clouds compared with the conventional method. These results improve the safety and operability of environmental awareness in applications such as robotics.

关键词： 3d semantic segmentation Point Completion Incomplete Point Clouds

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Adaptive Margin Contrastive Learning for Ambiguity-aware 3d semantic segmentation

Adaptive Margin Contrastive Learning for Ambiguity-aware 3D ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chen, Yang duane, Yueqi Zhang, Runzhong Tan, Yap-Peng Nanyang Technol Univ Singapore Singapore Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350390155;9798350390162

In this paper, we propose an adaptive margin contrastive learning method for 3d point cloud semantic segmentation, namely AMContrast3d. Most existing methods use equally penalized objectives, which ignore per-point ambiguities and less discriminated features stemming from transition regions. However, as highly ambiguous points may be indistinguishable even for humans, their manually annotated labels are less reliable, and hard constraints over these points would lead to suboptimal models. To address this, we design adaptive objectives for individual points based on their ambiguity levels, aiming to ensure the correctness of low-ambiguity points while allowing mistakes for high-ambiguity points. Specifically, we first estimate ambiguities based on position embeddings. Then, we develop a margin generator to shift decision boundaries for contrastive feature embeddings, so margins are narrowed due to increasing ambiguities with even negative margins for extremely highambiguity points. Experimental results on large-scale datasets, S3dIS and ScanNet, demonstrate that our method outperforms state-of-the-art methods.

关键词： 3d semantic segmentation 3d Scene Understanding Contrastive Learning decision Boundary

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