ResDLPS-Net: Joint residual-dense optimization for large-scale point cloud semantic segmentation

Du, Juan; Cai, Guorong; Wang, Zongyue; Huang, Shangfeng; Su, Jinhe; Marcato, José; Smit, Julian; Li, Jonathan

doi:10.1016/j.isprsjprs.2021.09.024

Cited by 28 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BAAFNet [32] involves a feature fusion module that uses an adaptive strategy to fuse features at different scales. ResDLPS-Net [35] presents a method for pairwise aggregation to effectively extract appropriate the neighborhood information. Meanwhile, MFA [34] utilizes dense skin connections to improve feature retention at the current resolution.…”

Section: Multi-scale Feature Fusionmentioning

confidence: 99%

“…However, there is a potential risk of losing the original information when expanding the perception region. To avoid this, inspired by ResDLPS-Net [35], we concatenate the outputs of the two blocks and update them through an MLP layer. Next, the input feature f i is also updated through an MLP layer and then summed with the prior fused output features, finally yielding the encoded feature fi :…”

Section: Dilated Bilateral Blockmentioning

confidence: 99%

“…Subsequently, the upsampling structure combines this information to provide a comprehensive description at multiple scales. Several methods [32][33][34][35] have been proposed to enhance this framework for more efficient usage. However, the existing methods tend to fuse information layer by layer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

BEMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Bilateral Neighbor Enhancement and Multi-Scale Fusion

Ji,

Yang,

Jiang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and holds great importance. However, achieving precise semantic segmentation represents a significant hurdle. In this paper, we present BEMF-Net, an efficient method for large-scale environments. It starts with an effective feature extraction method. Unlike images, 3D data comprise not only geometric relations but also texture information. To accurately depict the scene, it is crucial to take into account the impacts of texture and geometry on the task, and incorporate modifications to improve feature description. Additionally, we present a multi-scale feature fusion technique that effectively promotes the interaction between features at different resolutions. The approach mitigates the problem of the smoothing of detailed information caused by downsampling mechanisms, while ensuring the integrity of features across different layers, allowing a more comprehensive representation of the point cloud. We confirmed the effectiveness of this method by testing it on benchmark datasets such as S3DIS, SensatUrban, and Toronto3D.

show abstract

Section: Multi-scale Feature Fusionmentioning

confidence: 99%

Section: Dilated Bilateral Blockmentioning

confidence: 99%

See 1 more Smart Citation

BEMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Bilateral Neighbor Enhancement and Multi-Scale Fusion

Ji,

Yang,

Jiang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For example, Hu et al [30] combine self-attention mechanisms with a random sampling algorithm to design the RandLA-Net network. Du et al [31] add a dense convolutional linking layer on the basis of RandLA-Net for a more comprehensive learning of geometric shapes. LG-Net [32] achieves learning of global context information through a global correlation mining (GCM) module.…”

Section: Introductionmentioning

confidence: 99%

PointMM: Point Cloud Semantic Segmentation CNN under Multi-Spatial Feature Encoding and Multi-Head Attention Pooling

Chen,

Wu,

Luo

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

For the actual collected point cloud data, there are widespread challenges such as semantic inconsistency, density variations, and sparse spatial distribution. A network called PointMM is developed in this study to enhance the accuracy of point cloud semantic segmentation in complex scenes. The main contribution of PointMM involves two aspects: (1) Multi-spatial feature encoding. We leverage a novel feature encoding module to learn multi-spatial features from the neighborhood point set obtained by k-nearest neighbors (KNN) in the feature space. This enhances the network’s ability to learn the spatial structures of various samples more finely and completely. (2) Multi-head attention pooling. We leverage a multi-head attention pooling module to address the limitations of symmetric function-based pooling, such as maximum and average pooling, in terms of losing detailed feature information. This is achieved by aggregating multi-spatial and attribute features of point clouds, thereby enhancing the network’s ability to transmit information more comprehensively and accurately. Experiments on publicly available point cloud datasets S3DIS and ISPRS 3D Vaihingen demonstrate that PointMM effectively learns features at different levels, while improving the semantic segmentation accuracy of various objects. Compared to 12 state-of-the-art methods reported in the literature, PointMM outperforms the runner-up by 2.3% in OA on the ISPRS 3D Vaihingen dataset, and achieves the third best performance in both OA and MioU on the S3DIS dataset. Both achieve a satisfactory balance between OA, F1, and MioU.

show abstract

LFEA-Net: semantic segmentation for urban point cloud scene via local feature extraction and aggregation

Zeng,

Zhou,

et al. 2023

Urban Info

View full text Add to dashboard Cite

Considering the increasing prominence of 3D real city construction technology, 3D urban point cloud scene data merit further investigation. However, achieving finegrained semantic segmentation of urban scenes remains highly challenging due to the natural orderlessness and unstructured nature of acquired point clouds, along with their large-scale points and non-uniform distributions. In this study, we present LFEA-Net, a novel neural network specifically designed for semantic segmentation of large-scale urban point cloud scenes. The network comprises two main components: (1) The local feature extraction (LFE) module, which fully exploits local spatial, color and semantic information to enhance and preserve crucial information, consisting of bilateral feature encoding unit and multidimensional feature encoding unit. (2) The local feature aggregation (LFA) module, designed to bridge the semantic gap between local information and emphasize both local significant features and the entire local neighbor, consisting of soft cross operation and united pooling operation. We have evaluated the performance of LFEA-Net with state-of-the-art networks using the photogrammetric point cloud dataset SensatUrban, achieving 61.6 of mIoU score. The results demonstrate the superior efficacy of LFEA-Net in accurately segmenting and classifying large-scale urban point cloud scenes, highlighting its potential to advance environmental information perception.

show abstract

ResDLPS-Net: Joint residual-dense optimization for large-scale point cloud semantic segmentation

Cited by 28 publications

References 22 publications

BEMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Bilateral Neighbor Enhancement and Multi-Scale Fusion

BEMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Bilateral Neighbor Enhancement and Multi-Scale Fusion

PointMM: Point Cloud Semantic Segmentation CNN under Multi-Spatial Feature Encoding and Multi-Head Attention Pooling

LFEA-Net: semantic segmentation for urban point cloud scene via local feature extraction and aggregation

Contact Info

Product

Resources

About