Pith. sign in

REVIEW 4 major objections 5 minor 51 references

Interpolated Convolutional Networks for 3D Point Cloud Understanding

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read InterpConv, a convolution that interpolates point features onto discrete kernel weights, sets new accuracy marks on three 3D point cloud benchmarks.

desk verdict A solid, clearly-written point cloud convolution paper whose core idea (discrete kernels + interpolation) is real, but whose headline sparsity-invariance claim is asserted rather than demonstrated. read the letter →

arxiv 1908.04512 v1 pith:FLL6N5UH submitted 2019-08-13 cs.CV cs.CGeess.IV

Jiageng Mao , Xiaogang Wang , Hongsheng Li This is my paper · ORCID
classification cs.CVcs.CGeess.IV
keywords interpolatedconvolutionpointcloudclassificationsemanticsegmentationsparsityinvariancepermutationdiscretekernelweightsmulti-scalefeatures3Ddeeplearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces InterpConv, a convolution operation that runs directly on irregular 3D point clouds instead of voxel grids or graphs. Its central claim is that a spatially discrete set of kernel-weight vectors, combined with an interpolation function that pulls nearby point features onto those weights, gives point-cloud networks the same inductive bias that ordinary discrete kernels give image CNNs. A per-weight normalization term is meant to make the operation invariant to how densely points are sampled. The authors build classification and segmentation networks around this operation and report state-of-the-art accuracy on ModelNet40 (93.0%), ShapeNet Parts (86.3% instance mIoU), and S3DIS (66.7% mIoU). A sympathetic reader would take away that discrete convolutional kernels can be adapted to unstructured 3D data without giving up the regularity that makes convolution work.

What carries the argument

The central object is the InterpConv operator, written as $$F*W(\hat{p})=\sum_{p'} \frac{1}{N_{p'}}\sum_{p_\delta} T(p_\delta,p')\,F(\hat{p}+p_\delta)\cdot W(p'),$$ where $W(p')$ are discrete $1\times c$ kernel-weight vectors with fixed 3D coordinates $p'$, $T$ is an interpolation function (trilinear or Gaussian), and $N_{p'}$ is the density normalization term. The interpolation function is what lets discrete weights act on off-grid points; the normalization term is what carries the sparsity-invariance claim; the fixed cube arrangement of $p'$ is what lets standard receptive-field intuitions (kernel size, kernel length) transfer to point clouds.

What would settle it

Resample the same ModelNet40 shapes at, say, 256 and 8,192 points, run the trained InterpCNN on both without retraining, and compare accuracy and per-point features; a large drop at low density would refute the sparsity-invariance claim.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that a convolution kernel need not be tied to a regular grid to stay discrete. InterpConv places $n^3$ fixed $1\times c$ weight vectors at coordinates $p'$ of a cube in 3D space, then for each input point computes interpolation weights $T(p_\delta,p')$ from the point to nearby weight coordinates. Features are accumulated at each weight coordinate and divided by a normalization term $N_{p'}$ that counts neighboring points or sums their weights, which is what makes the aggregation independent of local density. The convolution at any output location is then the dot product of the normalized feature block with the discrete kernel weights. With trilinear or Gaussian interpolation, this operation is permutation invariant and can be evaluated at arbitrary centers, so it consumes raw point clouds directly while keeping standard convolutional machinery.

Load-bearing premise

The claim that the operation is unaffected by how densely points are sampled rests on the normalization term alone, yet the choice of which neighboring points contribute still depends on local point density.

Editorial extensions

If this is right

  • The operation consumes irregular point clouds directly, without voxelization, so fine geometry is preserved and dense-grid cost is avoided.
  • Multi-branch blocks with different kernel lengths capture both fine-grained local structures and global shape context in a single network.
  • A U-Net-style encoder-decoder built from InterpConv layers reaches state-of-the-art results on object part and indoor scene segmentation.
  • The per-weight normalization term makes the convolution invariant to local point density, addressing a weakness of graph-based neighbor selection.
  • Discrete kernels give point-cloud networks an inductive bias similar to image CNNs while using fewer parameters than continuous MLP-based kernels.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The sparsity-invariance claim is testable but not directly demonstrated: resampling the same shapes at very different densities and comparing features or accuracy would settle it, and the paper reports no such experiment.
  • Learnable kernel-weight coordinates, listed as future work, could let kernels adapt to anisotropic local geometry and likely improve segmentation further.
  • The same interpolation mechanism may transfer to 3D object detection and instance segmentation, where point density varies sharply across scenes.
  • Other interpolation bases, such as linear B-splines, could trade off localization and smoothness differently and are worth comparing to the two proposed functions.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes InterpConv, a convolution-like operation for 3D point clouds. Instead of rasterizing points into voxels or building KNN graphs, InterpConv places a regular grid of discrete kernel-weight vectors in 3D space and maps nearby input point features to those weights using either trilinear or Gaussian interpolation. A normalization term divides by the number of neighboring points or by the sum of interpolation weights, which the authors claim makes the operation permutation and sparsity invariant. The paper then builds classification and segmentation networks around InterpConv layers and reports state-of-the-art or competitive results on ModelNet40 (93.0% accuracy), ShapeNet Parts (86.3% instance mIoU), and S3DIS (66.7% mIoU), together with ablations of kernel size, kernel length, interpolation function, normalization, parameter count, and runtime.

Significance. If the claims are borne out, InterpConv is a simple and appealing alternative to graph-based and voxel-based point cloud convolutions: it avoids per-edge MLPs, has a clear formulation in Eq. (3), and the ablations in Section 4.4 isolate the roles of kernel size, kernel length, interpolation, and normalization. The authors also provide parameter-count and inference-time comparisons, which are useful for practitioners. However, the headline property of sparsity invariance is asserted rather than demonstrated, the benchmark margins over strong baselines are small, and no error bars or multiple-run statistics are reported. These issues bear directly on the central claims, so the paper needs additional evidence before it can be accepted as stated.

major comments (4)
  1. [Section 3.2, Eqs. (7)-(8); Section 4] The claim that InterpConv is sparsity invariant is not supported by the formulation or by experiment. Eqs. (7) and (8) normalize an accumulated sum, but the sum runs over the random set of points inside a hard-cutoff neighborhood: the 3σ ball of Eq. (6) or the kernel cell of Eq. (5). For finite point clouds this set depends on local density; in sufficiently sparse regions it can be empty, and the paper does not specify a fallback for the N=0 or Σt_i=0 case. Normalization by N or Σt_i converts the aggregation into a weighted average, but the realized weighted average still depends on which points fall inside the support, and its variance grows as density decreases. Section 3.3 further downsamples points after each 3×3×3 InterpConv in the segmentation network, so later layers operate on progressively sparser clouds. Section 4 contains no experiment that resamples or thins the input density, so the claimed advantage over KNN-based graph construction is not empirically established. I ask the authors to prove or qualify the invariance under a precise sampling model and to add a density-robustness experiment, for example uniform thinning and nonuniform density resampling.
  2. [Section 4, Tables 1-3 and 4-9] All benchmark numbers are reported as single runs without error bars or significance statements. The margins over the strongest baselines are small on ModelNet40 (0.8% over DGCNN/PointCNN in Table 1) and ShapeNet Parts (0.6% over PointConv in Table 2), and the S3DIS result is a single 6-fold mean with no fold-wise variance. Given typical run-to-run variation in point-cloud networks, these differences could be within noise. Please report mean and standard deviation over multiple runs, or at least fold-wise S3DIS results, and state whether the final architecture was selected on a validation split.
  3. [Section 4.4, Table 6] The interpolation function is chosen per task according to test-set accuracy: Gaussian is used for ModelNet40 and trilinear for ShapeNet Parts, with the selected numbers then reported as the final results. This uses the test set for model selection, which can bias the reported state-of-the-art. The choice should be made on a held-out validation split, or the paper should fix a single selection rule and report the accuracy of both interpolation functions on all tasks under that rule.
  4. [Section 4.1, Eq. (6), Table 5] In the classification network the Gaussian bandwidth is fixed to 3σ=0.1, while kernel lengths l range up to 0.8 in Table 5. Because Eq. (6) truncates weights beyond 3σ, points farther than 0.1 from every kernel-weight coordinate receive zero weight; for large l, a substantial fraction of points in the kernel volume may be ignored and some kernel-weight coordinates may have very few or no neighboring points. The paper does not explain how the normalization denominators in Eqs. (7)-(8) are handled in this case, nor whether σ is scaled with l. The effective receptive field is therefore not simply controlled by l. Please clarify the relationship between σ and l, or scale the support with the grid spacing.
minor comments (5)
  1. [Algorithm 1, lines 7-8] The pseudocode appears to normalize the accumulated feature inside the loop over neighboring points ("fi ← fi + t fp; fi ← Normalize(fi)"), which is inconsistent with Eq. (3). Normalization should occur after the full sum over the neighborhood is accumulated.
  2. [Eq. (5)] The text says that the input point and kernel weights are normalized into a unit-length cube, but the scaling steps are not written out. Please define the normalization explicitly before presenting the simplified trilinear formula.
  3. [Section 4.3, Table 3] Figure 4 shows a qualitative comparison with PointNet++, but PointNet++ is not included in Table 3; either add it to the table or adjust the caption and text.
  4. [Section 4.4, Table 5] The kernel-length triples (e.g., "0.05-0.1-0.2") are not explicitly mapped to the three branches of each PointInception module; please make this mapping clear in the table or text.
  5. [Section 4.1] The sentence "We further sample 1,024 points for training and testing" is ambiguous about whether this is a random subsample or a fixed choice; please specify.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are benchmark accuracies on external datasets, and the sparsity-invariance statement is a definitional design property rather than a fitted prediction.

full rationale

I find no circular derivation in this paper. The central results are classification and segmentation accuracies on ModelNet40, ShapeNet Parts, and S3DIS, which are external benchmarks with fixed evaluation protocols. No parameter is fitted to a subset of these benchmarks and then reported as a prediction of a closely related quantity. The proposed InterpConv operation is defined in Section 3.2, and the ablations in Tables 4-7 compare genuine architectural alternatives rather than renaming a fitted input as a result. The sparsity-invariance claim is stated as a design property of the normalization terms in Eqs. (7)-(8); it is asserted by construction rather than empirically stress-tested for finite point clouds with hard-cutoff neighborhoods, but that is an evidence or correctness concern, not a circular step. The choice of Gaussian versus trilinear interpolation per task in Table 6 is model selection on the test sets and may inflate reported numbers, but it does not make the derivation circular. There are no load-bearing self-citations: the cited prior works are external methods such as PointNet++, DGCNN, and PointConv. Therefore the paper's derivation chain is self-contained with respect to its empirical claims.

Assumptions & free parameters 2 free parameters · 2 assumptions · 0 invented entities

The method introduces discrete kernel-weight coordinates and an interpolation function, both computational constructs with no new physical entities. The main free parameters are kernel length l and Gaussian bandwidth; their values are chosen by hand per task and affect accuracy as shown in the ablations. The axioms are standard domain assumptions about point cloud density plus an explicit design choice to fix kernel coordinates.

free parameters (2)
  • Kernel length l = 0.1/0.2/0.4 for classification; 0.05-0.4 for segmentation
    Controls the Euclidean distance between adjacent kernel weight vectors. Table 5 shows accuracy varies by 1.1 percentage points as l is scaled, so this hand-set hyperparameter materially affects the reported results.
  • Gaussian bandwidth 3σ = 0.1
    Fixed to 0.1 in all classification InterpConv blocks, setting the neighborhood radius for feature interpolation. No ablation is provided for this value, yet it determines how many points contribute to each kernel weight.
assumptions (2)
  • domain assumption Point clouds are sampled densely enough that interpolation between discrete kernel weights and neighboring points captures local geometry.
    The method assumes a fixed spatial arrangement of kernel weights (Eq. 4) and an interpolation function that assigns features from nearby points; in extremely sparse regions, a kernel weight may receive no points at all, and the normalization term cannot compensate for empty neighborhoods.
  • ad hoc to paper Fixed kernel-weight coordinates (regular 3x3x3 grid) are sufficient for state-of-the-art accuracy.
    The paper fixes p' in all experiments (Section 3.2) and only mentions learnable coordinates as future work (Section 5). The performance claim depends on this hand-set choice, which is not learned or adapted to the input shape.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Interpolated Convolutional Networks for 3D Point Cloud Understanding." pith.science (2026). https://pith.science/paper/FLL6N5UH

@misc{pith2026190804512,
  author       = {Pith},
  title        = {Pith review of: Interpolated Convolutional Networks for 3D Point Cloud Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FLL6N5UH}},
  note         = {Machine review of arXiv:1908.04512}
}
read the original abstract

Point cloud is an important type of 3D representation. However, directly applying convolutions on point clouds is challenging due to the sparse, irregular and unordered data structure. In this paper, we propose a novel Interpolated Convolution operation, InterpConv, to tackle the point cloud feature learning and understanding problem. The key idea is to utilize a set of discrete kernel weights and interpolate point features to neighboring kernel-weight coordinates by an interpolation function for convolution. A normalization term is introduced to handle neighborhoods of different sparsity levels. Our InterpConv is shown to be permutation and sparsity invariant, and can directly handle irregular inputs. We further design Interpolated Convolutional Neural Networks (InterpCNNs) based on InterpConv layers to handle point cloud recognition tasks including shape classification, object part segmentation and indoor scene semantic parsing. Experiments show that the networks can capture both fine-grained local structures and global shape context information effectively. The proposed approach achieves state-of-the-art performance on public benchmarks including ModelNet40, ShapeNet Parts and S3DIS.

Figures

Figures reproduced from arXiv: 1908.04512 by the authors.

Figure 1
Figure 1. Illustration of different types of convolutions on point [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Interpolated Convolutional Neural Networks (InterpCNNs). Gray boxes indicate the size of input and output data, and other [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Visualization of object part segmentation results on [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Visualization of feature activations learned by different [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Visualization of failure cases on ShapeNet Parts. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 38 canonical work pages

  1. [1]

    3d semantic parsing of large-scale indoor spaces

    Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese. 3d semantic parsing of large-scale indoor spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion, pages 1534–1543, 2016. 2, 7

    work page 2016

  2. [2]

    Point convolutional neural networks by extension operators

    Matan Atzmon, Haggai Maron, and Yaron Lipman. Point convolutional neural networks by extension operators. arXiv preprint arXiv:1803.10091, 2018. 6

    arXiv 2018

  3. [3]

    3d point cloud classification and segmentation using 3d modified fisher vector representation for convolutional neu- ral networks

    Yizhak Ben-Shabat, Michael Lindenbaum, and Anath Fis- cher. 3d point cloud classification and segmentation using 3d modified fisher vector representation for convolutional neu- ral networks. arXiv preprint arXiv:1711.08241, 2017. 6

    arXiv 2017

  4. [4]

    Generative and discriminative voxel mod- eling with convolutional neural networks

    Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston. Generative and discriminative voxel mod- eling with convolutional neural networks. arXiv preprint arXiv:1608.04236, 2016. 6

    arXiv 2016

  5. [5]

    Shapenet: An information-rich 3d model repository

    Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2, 5

    arXiv 2015

  6. [6]

    Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

    Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834–848, 2018. 3

    work page 2018

  7. [7]

    Multi-view 3d object detection network for autonomous driving

    Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, and Tian Xia. Multi-view 3d object detection network for autonomous driving. In Proceedings of the IEEE Conference on Com- puter Vision and Pattern Recognition , pages 1907–1915,

    work page 1907

  8. [8]

    3dcapsule: Extending the capsule architecture to classify 3d point clouds

    Ali Cheraghian and Lars Petersson. 3dcapsule: Extending the capsule architecture to classify 3d point clouds. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1194–1202. IEEE, 2019. 2, 5, 6

    work page 2019

Show all 51 references
  1. [9]

    Deformable convolutional networks

    Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international confer- ence on computer vision, pages 764–773, 2017. 3

    2017

  2. [10]

    3d semantic segmentation with submanifold sparse convolutional networks

    Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 3d semantic segmentation with submanifold sparse convolutional networks. In Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition, pages 9224–9232, 2018. 6

    2018

  3. [11]

    Point- wise convolutional neural networks

    Binh-Son Hua, Minh-Khoi Tran, and Sai-Kit Yeung. Point- wise convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion, pages 984–993, 2018. 4, 6

    2018

  4. [12]

    Re- current slice networks for 3d segmentation of point clouds

    Qiangui Huang, Weiyue Wang, and Ulrich Neumann. Re- current slice networks for 3d segmentation of point clouds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2626–2635, 2018. 2, 6, 7

    2018

  5. [13]

    Batch normalization: Accelerating deep network training by reducing internal co- variate shift

    Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal co- variate shift. arXiv preprint arXiv:1502.03167, 2015. 5

    2015 arXiv

  6. [14]

    Pointsift: A sift- like network module for 3d point cloud semantic segmenta- tion

    Mingyang Jiang, Yiran Wu, and Cewu Lu. Pointsift: A sift- like network module for 3d point cloud semantic segmenta- tion. arXiv preprint arXiv:1807.00652, 2018. 2

    2018 arXiv

  7. [15]

    Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints

    Asako Kanezaki, Yasuyuki Matsushita, and Yoshifumi Nishida. Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5010–5019, 2018. 2

    2018

  8. [16]

    Escape from cells: Deep kd-networks for the recognition of 3d point cloud mod- els

    Roman Klokov and Victor Lempitsky. Escape from cells: Deep kd-networks for the recognition of 3d point cloud mod- els. In Proceedings of the IEEE International Conference on Computer Vision, pages 863–872, 2017. 2, 6

    2017

  9. [17]

    Imagenet classification with deep convolutional neural net- works

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural net- works. In Advances in neural information processing sys- tems, pages 1097–1105, 2012. 3

    2012

  10. [18]

    Large-scale point cloud semantic segmentation with superpoint graphs

    Loic Landrieu and Martin Simonovsky. Large-scale point cloud semantic segmentation with superpoint graphs. InPro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4558–4567, 2018. 7

    2018

  11. [19]

    So-net: Self- organizing network for point cloud analysis

    Jiaxin Li, Ben M Chen, and Gim Hee Lee. So-net: Self- organizing network for point cloud analysis. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 9397–9406, 2018. 2, 6

    2018

  12. [20]

    Pointcnn: Convolution on x-transformed points

    Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. Pointcnn: Convolution on x-transformed points. In Advances in Neural Information Processing Sys- tems, pages 828–838, 2018. 1, 2, 3, 6, 7

    2018

  13. [21]

    Fpnn: Field probing neural networks for 3d data

    Yangyan Li, Soeren Pirk, Hao Su, Charles R Qi, and Leonidas J Guibas. Fpnn: Field probing neural networks for 3d data. In Advances in Neural Information Processing Systems, pages 307–315, 2016. 2

    2016

  14. [22]

    Gated graph sequence neural networks

    Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493, 2015. 2

    2015 arXiv

  15. [23]

    Point2sequence: Learning the shape representa- tion of 3d point clouds with an attention-based sequence to sequence network

    Xinhai Liu, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker. Point2sequence: Learning the shape representa- tion of 3d point clouds with an attention-based sequence to sequence network. arXiv preprint arXiv:1811.02565, 2018. 2, 5, 6

    2018 arXiv

  16. [24]

    V oxnet: A 3d con- volutional neural network for real-time object recognition

    Daniel Maturana and Sebastian Scherer. V oxnet: A 3d con- volutional neural network for real-time object recognition. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 922–928. IEEE, 2015. 1, 2, 4

    2015

  17. [25]

    Pointnet: Deep learning on point sets for 3d classification and segmentation

    Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 652–660,

  18. [26]

    V olumetric and multi-view cnns for object classification on 3d data

    Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas. V olumetric and multi-view cnns for object classification on 3d data. In Pro- ceedings of the IEEE conference on computer vision and pat- tern recognition, pages 5648–5656, 2016. 6, 8

    2016

  19. [27]

    Pointnet++: Deep hierarchical feature learning on point sets in a metric space

    Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Infor- mation Processing Systems, pages 5099–5108, 2017. 2, 5, 6, 8

    2017

  20. [28]

    3d graph neural networks for rgbd semantic seg- mentation

    Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, and Raquel Urtasun. 3d graph neural networks for rgbd semantic seg- mentation. In Proceedings of the IEEE International Con- ference on Computer Vision, pages 5199–5208, 2017. 1, 2

    2017

  21. [29]

    Fully-convolutional point networks for large-scale point clouds

    Dario Rethage, Johanna Wald, Jurgen Sturm, Nassir Navab, and Federico Tombari. Fully-convolutional point networks for large-scale point clouds. In Proceedings of the Euro- pean Conference on Computer Vision (ECCV) , pages 596– 611, 2018. 2, 6

    2018

  22. [30]

    Octnet: Learning deep 3d representations at high resolutions

    Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger. Octnet: Learning deep 3d representations at high resolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3577–3586, 2017. 2, 6

    2017

  23. [31]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. In International Conference on Medical image com- puting and computer-assisted intervention , pages 234–241. Springer, 2015. 5, 6

    2015

  24. [32]

    Towards 3d point cloud based object maps for household environments

    Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Mi- hai Dolha, and Michael Beetz. Towards 3d point cloud based object maps for household environments. Robotics and Au- tonomous Systems, 56(11):927–941, 2008. 1

    2008

  25. [33]

    The graph neural network model

    Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Ha- genbuchner, and Gabriele Monfardini. The graph neural network model. IEEE Transactions on Neural Networks , 20(1):61–80, 2009. 2

    2009

  26. [34]

    Min- ing point cloud local structures by kernel correlation and graph pooling

    Yiru Shen, Chen Feng, Yaoqing Yang, and Dong Tian. Min- ing point cloud local structures by kernel correlation and graph pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4548–4557,

  27. [35]

    Dynamic edge- conditioned filters in convolutional neural networks on graphs

    Martin Simonovsky and Nikos Komodakis. Dynamic edge- conditioned filters in convolutional neural networks on graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3693–3702, 2017. 6

    2017

  28. [36]

    Splatnet: Sparse lattice networks for point cloud processing

    Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, and Jan Kautz. Splatnet: Sparse lattice networks for point cloud processing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2530–2539, 2018. 6

    2018

  29. [37]

    Multi-view convolutional neural networks for 3d shape recognition

    Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE in- ternational conference on computer vision , pages 945–953,

  30. [38]

    Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1–9, 2015. 5, 6

    2015

  31. [39]

    Tangent convolutions for dense prediction in 3d

    Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, and Qian- Yi Zhou. Tangent convolutions for dense prediction in 3d. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3887–3896, 2018. 1

    2018

  32. [40]

    Dominant set clustering and pooling for multi-view 3d object recogni- tion

    Chu Wang, Marcello Pelillo, and Kaleem Siddiqi. Dominant set clustering and pooling for multi-view 3d object recogni- tion. In Proceedings of British Machine Vision Conference (BMVC), volume 12, 2017. 2

    2017

  33. [41]

    Local spec- tral graph convolution for point set feature learning

    Chu Wang, Babak Samari, and Kaleem Siddiqi. Local spec- tral graph convolution for point set feature learning. In Pro- ceedings of the European Conference on Computer Vision (ECCV), pages 52–66, 2018. 6

    2018

  34. [42]

    V oting for voting in online point cloud object detection

    Dominic Zeng Wang and Ingmar Posner. V oting for voting in online point cloud object detection. In Robotics: Science and Systems, volume 1, pages 10–15607, 2015. 2

    2015

  35. [43]

    Deep parametric continu- ous convolutional neural networks

    Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, and Raquel Urtasun. Deep parametric continu- ous convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion, pages 2589–2597, 2018. 1, 2, 3

    2018

  36. [44]

    Sgpn: Similarity group proposal network for 3d point cloud instance segmentation

    Weiyue Wang, Ronald Yu, Qiangui Huang, and Ulrich Neu- mann. Sgpn: Similarity group proposal network for 3d point cloud instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 2569–2578, 2018. 6, 7

    2018

  37. [45]

    Dynamic graph cnn for learning on point clouds

    Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. arXiv preprint arXiv:1801.07829, 2018. 1, 2, 3, 5, 6, 7, 8

    2018 arXiv

  38. [46]

    Pointconv: Deep convolutional networks on 3d point clouds

    Wenxuan Wu, Zhongang Qi, and Li Fuxin. Pointconv: Deep convolutional networks on 3d point clouds. arXiv preprint arXiv:1811.07246, 2018. 1, 3, 6

    2018 arXiv

  39. [47]

    At- tentional shapecontextnet for point cloud recognition

    Saining Xie, Sainan Liu, Zeyu Chen, and Zhuowen Tu. At- tentional shapecontextnet for point cloud recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4606–4615, 2018. 6, 7

    2018

  40. [48]

    Empirical evaluation of rectified activations in convolutional network

    Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853, 2015. 5

    2015 arXiv

  41. [49]

    Spidercnn: Deep learning on point sets with parameterized convolutional filters

    Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao. Spidercnn: Deep learning on point sets with parameterized convolutional filters. In Proceedings of the European Con- ference on Computer Vision (ECCV) , pages 87–102, 2018. 1, 2, 3, 6

    2018

  42. [50]

    A scalable active framework for re- gion annotation in 3d shape collections

    Li Yi, Vladimir G Kim, Duygu Ceylan, I Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, Leonidas Guibas, et al. A scalable active framework for re- gion annotation in 3d shape collections. ACM Transactions on Graphics (TOG), 35(6):210, 2016. 2, 6

    2016

  43. [51]

    Sync- speccnn: Synchronized spectral cnn for 3d shape segmenta- tion

    Li Yi, Hao Su, Xingwen Guo, and Leonidas J Guibas. Sync- speccnn: Synchronized spectral cnn for 3d shape segmenta- tion. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2282–2290, 2017. 6

    2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.