Sketchy Bounding-box Supervision for 3D Instance Segmentation

Jian Yang; Jin Xie; Le Hui; Qian Deng

REVIEW 4 major objections 6 minor 52 references

Sketchy Bounding-box Supervision for 3D Instance Segmentation

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Training on inaccurate 'sketchy' bounding boxes yields state-of-the-art 3D instance segmentation, rivaling fully supervised methods.

desk verdict A worthwhile new setting and a sensible method, but the paper's headline claim about state-of-the-art under sketchy boxes is not yet supported by the comparisons it reports. read the letter →

arxiv 2505.16399 v1 pith:HQ7U6JGG submitted 2025-05-22 cs.CV

Qian Deng , Le Hui , Jin Xie , Jian Yang This is my paper · ORCID

classification cs.CV

keywords 3Dinstancesegmentationweaklysupervisedlearningsketchyboundingboxespseudolabelingpointcloudcoarse-to-fineScanNetS3DIS

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

Sketchy-3DIS tackles the practical reality that 3D bounding-box annotations are rarely precise: annotators introduce scaling, translation, and rotation errors. The paper claims that a joint box-to-point pseudo labeler and a coarse-to-fine query-based segmentator can turn such inaccurate boxes into high-quality per-point instance labels. On ScanNetV2 and S3DIS, the method reaches state-of-the-art results under this sketchy-box supervision and, in several settings, outperforms fully supervised baselines. The practical payoff is annotation cost: labeling a 3D box takes roughly 1.93 minutes per scene, versus 22.3 minutes for point-level instance labels.

What carries the argument

The machinery is a two-module joint-training loop. The adaptive box-to-point pseudo labeler partitions points into background, single-box, and overlap sets; it filters background by the similarity $s_{p,B} = \cos(f_p, f_B) e^{-|c_B - c_p|}$, and assigns overlap points with an MLP that compares point features to the features of the reliable (non-overlap) parts of each box. The coarse-to-fine instance segmentator uses query-based attention over the whole scene, then over regions inside a predicted box and a contracted 'core' box, in Multi-level Attention Blocks. The two modules are supervised by a cross-entropy pseudo-label loss and an instance segmentation loss, paired by Hungarian matching.

What would settle it

Collect bounding boxes drawn quickly by human annotators on ScanNet or S3DIS, measure the actual scale, translation, and rotation error distribution, and retrain or re-evaluate Sketchy-3DIS on those annotations; if AP50 falls below the fully supervised baseline or by more than the gap claimed for synthetic sketchy boxes, the central robustness claim is falsified.

Watch

Extended reading notes

Core claim

The central claim is that instance segmentation can be learned from sketchy bounding boxes—boxes perturbed by 5% scaling, 5% translation, and 5 degrees rotation from ground truth—without sacrificing competitive accuracy. The paper shows that existing box-supervised methods lose substantial performance under such perturbations, while Sketchy-3DIS maintains state-of-the-art results by converting the rough boxes into compact pseudo instance labels. The conversion is adaptive: points inside overlapping boxes are assigned by a learned point-to-box similarity, and background points inside otherwise reliable boxes are filtered by a coordinate-plus-feature similarity score. A coarse-to-fine segmentator then refines instances by attending to scene-level, coarse-region, and core-region features, and joint training with the pseudo labeler progressively improves both. On the ScanNetV2 validation split the method reaches 65.8 AP50 with sketchy boxes (68.8 with accurate boxes), and on S3DIS Area 5 it reaches 64.6 AP50 with sketchy boxes, exceeding several fully supervised baselines.

Load-bearing premise

The claim rests on two linked premises: synthetic perturbations (5% scale, 5% translation, 5 degrees rotation) faithfully model real-world sketchy boxes, and points that fall inside only one box are clean enough to bootstrap the overlap assignment.

Editorial extensions

If this is right

Weakly supervised 3D instance segmentation can work from loose, inaccurate boxes, reducing annotation effort to roughly the cost of drawing a box per object rather than labeling every point.
Existing box-supervised methods lose a large slice of performance under sketchy boxes, so evaluating robustness to annotation noise becomes a necessary part of the comparison.
Joint training of the pseudo labeler and the segmentator clearly beats a disjoint pipeline (AP rises from 60.4 to 69.1 on S3DIS Area 5), so the two components should be optimized together.
The method's advantage holds on two independent indoor benchmarks, ScanNetV2 and S3DIS, indicating the approach is not tuned to a single dataset.
Even with accurate boxes the method remains competitive with the strongest weakly supervised baselines, so adopting sketchy-box training does not give up the clean-annotation regime.

Reading between the lines

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

The synthetic perturbation model (uniform scaling, translation, rotation) is a proxy for human annotation errors; real 'sketchy' boxes may have correlated or class-dependent errors, so a natural test is to benchmark on manually drawn loose boxes rather than perturbed ground truth.
The method's ability to segment objects missing from the ground truth (e.g., cabinets in Fig. 6) hints it could serve as a noise-tolerant annotator that discovers unlabeled instances, not merely a recognizer of annotated ones.
Because the pseudo labeler resolves overlap only between two boxes, scenes with heavy multi-object overlap (e.g., clutter) may need higher-order assignment; extending the MLP to $k$-box overlaps is a direct testable extension.
The degradation from S1 to S4 (65.8 to 62.5 AP50) suggests there is a perturbation budget; measuring the method's tolerance as a function of $\alpha$, $\beta$, $\gamma$ would let practitioners decide when to invest in more careful box annotation.

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.

Desk Editor's Note

read the letter

The paper introduces a new weak supervision setting for 3D instance segmentation: sketchy bounding boxes, which are ground-truth boxes perturbed with scaling, translation, and rotation. The setting is genuinely new relative to the cited prior work, which assumes accurate or slightly loose boxes, and the proposed method (adaptive box-to-point pseudo labeler plus coarse-to-fine segmentator) is clearly described and carefully ablated. The robustness table from accurate to heavily perturbed boxes is a good addition, and the gains over Box2Mask and GaPro under the same S1 perturbation on ScanNetV2 are real (65.8 vs 53.5 AP50 for GaPro).

The main weakness is that the central claim—state-of-the-art under sketchy boxes on both benchmarks—is not actually established by the tables. On S3DIS, every baseline is evaluated only under accurate boxes (S0); the only S1 row is Sketchy-3DIS itself. On ScanNet, the stronger weak baselines BSNet and CIP-WPIS are also only reported under S0, with only Box2Mask and GaPro given S1 numbers. Since GaPro loses about 17 AP50 points when moving from S0 to S1, you cannot assume the other baselines would stay at their S0 values. It is entirely possible that BSNet or CIP-WPIS, retrained under S1, would match or exceed the reported 65.8 AP50. The paper needs to run those baselines under S1 to support its headline.

A second, smaller but more clear-cut issue: the abstract claims the method "even outperforms several fully supervised methods" using sketchy boxes. The tables do not show that. On ScanNet, fully supervised SPFormer reaches 73.9 AP50; Sketchy-3DIS under S1 gets 65.8. On S3DIS, fully supervised ISBNet reaches 65.8 AP50; Sketchy-3DIS under S1 gets 64.6. That claim should be corrected or removed.

A minor point: the sketchy boxes are all synthetic perturbations of GT boxes (5% scaling, 5% translation, 5 degrees rotation). Real annotation noise may be larger and class-correlated, so the robustness might not transfer. There are also no error bars, so we don't know run-to-run variance. These are fixable and do not undermine the core idea.

Overall, this is a solid engineering contribution with a new problem formulation. It deserves a serious referee. The revision should add S1 comparisons for BSNet and CIP-WPIS, fix the abstract, and ideally include a test on a realistic noisy annotation set.

Referee Report

4 major / 6 minor

Summary. The paper proposes Sketchy-3DIS, a weakly supervised 3D instance segmentation method that trains from bounding box annotations perturbed by scaling, translation, and rotation ("sketchy boxes"). The framework combines an adaptive box-to-point pseudo labeler, which assigns points in overlapped boxes to instances and filters background points, with a coarse-to-fine instance segmentator supervised by the resulting pseudo labels. Experiments are reported on ScanNetV2 and S3DIS, and the paper claims state-of-the-art performance under sketchy box supervision, even surpassing some fully supervised methods. The code is made available.

Significance. If the claimed results are substantiated, the paper would make a practically valuable contribution by relaxing the requirement for accurate, compact box annotations in weakly supervised 3D instance segmentation, since real annotations are often imprecise. The proposed adaptive point-to-box assignment for overlapping boxes is a plausible and interesting idea, and the study addresses a realistic annotation scenario not covered by prior box-supervised methods. The paper also provides code and evaluates on standard benchmarks. However, as presented, the evaluation does not yet establish the headline claim because the comparison under the sketchy-box setting is incomplete on both datasets, and the abstract overstates the results relative to the tables.

major comments (4)

[§4.2, Table 2] The S3DIS Area 5 comparison does not include any competing weak-supervision method evaluated under S1 sketchy boxes; the only S1 row is Sketchy-3DIS itself (50.7 AP / 64.6 AP50). Therefore the statement that the method achieves leading performance under sketchy bounding box annotations is unsupported for S3DIS. Please add S1 results for Box2Mask, WISGP, GaPro, BSNet, and CIP-WPIS (or justify why they cannot be evaluated) using the same perturbation parameters as the proposed method.
[§4.2, Table 1] The ScanNetV2 S1 comparison is restricted to Box2Mask and GaPro; the stronger weak-supervision baselines BSNet and CIP-WPIS are reported only under S0 accurate boxes. Since GaPro's AP50 drops by roughly 17 points when moving from S0 to S1 (70.4 to 53.5), S0 numbers cannot be used to estimate S1 performance. Please report S1 results for BSNet, CIP-WPIS, and any other available strong baselines, and provide test-set S1 numbers as well, so that the "leading performance under sketchy boxes" claim can be evaluated.
[Abstract and §4.2/§5] The claim that the method "even outperforms several fully supervised methods using sketchy bounding boxes" is contradicted by the reported tables. Under S1, the proposed method's AP50 (65.8 on ScanNetV2 val, 64.6 on S3DIS) is lower than every fully supervised method listed in Tables 1 and 2. The only case where it exceeds a fully supervised method (ISBNet by +3.3 AP50 on S3DIS) is the S0 row in Table 2. Please correct the claim or explicitly state that the fully supervised comparison is under S0 accurate boxes, not under S1 sketchy boxes.
[§3.2 and Table 3] The exact generation of the S1–S4 sketchy boxes is not specified. The text defines alpha=5%, beta=5%, and gamma=5 for the basic scaling, translation, and rotation operations, but does not state how these are combined to produce S2, S3, and S4. Without this definition, the robustness results in Table 3 cannot be reproduced. Please provide the precise combinatorial procedures and parameter values for all four sketchy levels.

minor comments (6)

[§4.2] There is a typo: "SKeychy-3DIS" should be "Sketchy-3DIS".
[Table 2] The two Sketchy-3DIS rows are not labeled with the supervision type in the "Sup." column; the S0 row has a blank entry. Please label both rows explicitly as S0 and S1 for clarity.
[Table 1] The ScanNet test row for Sketchy-3DIS (70.1 AP50, 86.6 AP25) is reported without a sup label. Please clarify whether this result was obtained under S0 or S1 sketchy boxes, since the abstract's claim about sketchy boxes depends on this distinction.
[§3.3, Eq. (3)] The notation e^{-|cB - cp|} does not specify the norm used for the coordinate difference; please define it (e.g., Euclidean distance) and its units.
[§3.4, Eq. (6)] The thresholding operation sigma is not described; please define the threshold value and how it is applied to the masked features.
[§5] The conclusion states that performance "degraded severely when the annotated sketchy bounding boxes are immensely inaccurate," but the experiments only test S1–S4, which are combinations of the small basic perturbations (5% scaling, 5% translation, 5 degrees rotation). This statement is not supported by the reported results; please either add experiments with larger perturbations or soften the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the pseudo-labeling loop is anchored to external sketchy box annotations; the main caveat is an incomplete S1 baseline comparison, which is a benchmarking issue, not a circular derivation.

full rationale

The derivation chain is self-contained against external box annotations and held-out instance masks. Sketchy-3DIS takes ground-truth boxes perturbed by scaling, translation, and rotation (Sec. 3.2, Eq. 2) and converts them to point-level pseudo labels via spatial/feature similarity (Eq. 3) and an MLP trained on reliable points (Eqs. 4-5); the segmentator is supervised by those pseudo labels through Hungarian matching (Eqs. 8-10) and evaluated on held-out validation instances. No step predicts a quantity that was itself used as a fitted input: the pseudo labeler's MLP is trained on points lying in a single box, with labels defined by box membership, and is applied to overlapping points—this is label propagation from the same external boxes, not a self-referential prediction. The hand-set perturbation parameters (alpha=5%, beta=5%, gamma=5) mean the reported 'sketchy' results are conditional on a synthetic noise model, which is an external-validity limitation rather than circularity. The conclusion itself acknowledges that performance degrades severely for immensely inaccurate boxes. The absence of S1 rows for many competitors in Tables 1 and 2 undermines the claim of leading performance under sketchy boxes, but that is a comparison-completeness problem, not a case of a result reducing to its own input. Citations to prior work by the authors (e.g., [13]) are used only as fully supervised baselines and are not load-bearing for the central claim.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The method does not postulate new physical entities. Its free parameters are the perturbation schedule that defines the benchmark and standard loss weights. The key assumptions are domain-level: the simulation fidelity of the sketchy boxes and the reliability of non-overlapping points.

free parameters (2)

sketchy box perturbation parameters (alpha, beta, gamma) = alpha=5%, beta=5%, gamma=5 degrees
Introduced in Sec. 3.2 to simulate inaccurate boxes. They define the evaluation regime, and the paper states they are 'empirically set' to small values. The robustness table S0-S4 varies these, but only in aggregate.
loss weights (lambda1, lambda2, lambda3) = 0.5, 1.0, 0.5
Set in Sec. 4.1 for matching cost and training loss. They are hand-selected hyperparameters that influence the reported AP values.

assumptions (3)

domain assumption Perturbing ground-truth boxes with scaling, translation, and rotation at the stated magnitudes produces boxes representative of real 'sketchy' annotations.
This is the core assumption of the evaluation. Sec. 3.2 defines the perturbation model; Sec. 4.2 evaluates only on these synthetic boxes. There is no experiment with real human-annotated inaccurate boxes.
domain assumption Points contained in a single sketchy box are mostly object points and form a reliable training set for the overlap classifier.
Sec. 3.3 uses points in B_rel (single-box regions) as reliable labels for the MLP that assigns overlapping points. If many single-box points are background or the box is very loose, this assumption weakens.
domain assumption Points outside all boxes are background.
Sec. 3.3 directly labels points outside boxes as background. This is standard for box supervision but can hide objects not annotated in the input boxes.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Sketchy Bounding-box Supervision for 3D Instance Segmentation." pith.science (2026). https://pith.science/paper/HQ7U6JGG

@misc{pith2026250516399,
  author       = {Pith},
  title        = {Pith review of: Sketchy Bounding-box Supervision for 3D Instance Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HQ7U6JGG}},
  note         = {Machine review of arXiv:2505.16399}
}

read the original abstract

Bounding box supervision has gained considerable attention in weakly supervised 3D instance segmentation. While this approach alleviates the need for extensive point-level annotations, obtaining accurate bounding boxes in practical applications remains challenging. To this end, we explore the inaccurate bounding box, named sketchy bounding box, which is imitated through perturbing ground truth bounding box by adding scaling, translation, and rotation. In this paper, we propose Sketchy-3DIS, a novel weakly 3D instance segmentation framework, which jointly learns pseudo labeler and segmentator to improve the performance under the sketchy bounding-box supervisions. Specifically, we first propose an adaptive box-to-point pseudo labeler that adaptively learns to assign points located in the overlapped parts between two sketchy bounding boxes to the correct instance, resulting in compact and pure pseudo instance labels. Then, we present a coarse-to-fine instance segmentator that first predicts coarse instances from the entire point cloud and then learns fine instances based on the region of coarse instances. Finally, by using the pseudo instance labels to supervise the instance segmentator, we can gradually generate high-quality instances through joint training. Extensive experiments show that our method achieves state-of-the-art performance on both the ScanNetV2 and S3DIS benchmarks, and even outperforms several fully supervised methods using sketchy bounding boxes. Code is available at https://github.com/dengq7/Sketchy-3DIS.

Figures

Figures reproduced from arXiv: 2505.16399 by the authors.

**Figure 2.** Various sketchy bounding boxes under scaling, transla [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** The framework of the Sketchy-3DIS. Given a point cloud with sketchy bounding-box annotations, we first extract the backbone features using a 3D U-Net backbone, then feed them into the adaptive box-to-point pseudo labeler and the coarse-to-fine instance segmentator, finally, we utilize the generated high quality pseudo labels to supervise the predicted instances periodically. types of sketchy bounding boxes, named S1… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The details of Multi-level Attention Block. The instance queries interact with the features of the whole scene, the coarse instance regions, and the instance core regions hierarchically. And the Bmask are the boxes obtained from the predicted masks. where fp − fBrel 1 …

**Figure 5.** Figure 5: Visualization comparison of pseudo labels on the ScanNetV2 training set. The black denotes the background points and other colors denote different objects. The green and red cycles highlight the key regions [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Qualitative results on ScanNetV2 validation set. The black denotes the background points and other colors denote different objects. The cycles highlight the key regions. validation set are shown in Fig.6. It can be observed that our method shows consistent effectivene…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

52 extracted references · 49 canonical work pages

[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 CVPR, pages 1534– 1543, 2016. 6

work page 2016
[2]

Hierarchical aggregation for 3D instance segmentation

Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, and Xinggang Wang. Hierarchical aggregation for 3D instance segmentation. In CVPR, pages 15467–15476, 2021. 1, 2

work page 2021
[3]

Masked-attention mask trans- former for universal image segmentation

Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask trans- former for universal image segmentation. In CVPR, pages 1290–1299, 2022. 2

work page 2022
[4]

Box2Mask: Weakly supervised 3D semantic instance segmentation using bounding boxes

Julian Chibane, Francis Engelmann, Tuan Anh Tran, and Ger- ard Pons-Moll. Box2Mask: Weakly supervised 3D semantic instance segmentation using bounding boxes. InECCV, pages 681–699. Springer, 2022. 1, 2, 3, 6, 7

work page 2022
[5]

Twist: Two-way inter-label self-training for semi-supervised 3D instance seg- mentation

Ruihang Chu, Xiaoqing Ye, Zhengzhe Liu, Xiao Tan, Xi- aojuan Qi, Chi-Wing Fu, and Jiaya Jia. Twist: Two-way inter-label self-training for semi-supervised 3D instance seg- mentation. In CVPR, pages 1100–1109, 2022. 1, 2

work page 2022
[6]

ScanNet: Richly- annotated 3D reconstructions of indoor scenes

Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly- annotated 3D reconstructions of indoor scenes. In CVPR, pages 5828–5839, 2017. 1, 6

work page 2017
[7]

Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision

Shichao Dong, Ruibo Li, Jiacheng Wei, Fayao Liu, and Gu- osheng Lin. Rwseg: Cross-graph competing random walks for weakly supervised 3D instance segmentation. arXiv preprint arXiv:2208.05110, 2022. 1, 2

work page Pith review arXiv 2022
[8]

Weakly-supervised point cloud instance segmentation with geometric priors

Heming Du, Xin Yu, Farookh Hussain, Mohammad Ali Armin, Lars Petersson, and Weihao Li. Weakly-supervised point cloud instance segmentation with geometric priors. In WACV, pages 4271–4280, 2023. 1, 2, 3, 6, 7

work page 2023

Show all 52 references

[9]

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 CVPR, pages 9224–9232,
[10]

Dyco3D: Robust instance segmentation of 3D point clouds through dynamic convolution

Tong He, Chunhua Shen, and Anton Van Den Hengel. Dyco3D: Robust instance segmentation of 3D point clouds through dynamic convolution. In CVPR, pages 354–363,
[11]

3D-SIS: 3D semantic instance segmentation of rgb-d scans

Ji Hou, Angela Dai, and Matthias Nießner. 3D-SIS: 3D semantic instance segmentation of rgb-d scans. In CVPR, pages 4421–4430, 2019. 2

2019
[12]

Exploring data-efficient 3D scene understanding with contrastive scene contexts

Ji Hou, Benjamin Graham, Matthias Nießner, and Saining Xie. Exploring data-efficient 3D scene understanding with contrastive scene contexts. In CVPR, pages 15587–15597,
[13]

Learning superpoint graph cut for 3D instance segmentation

Le Hui, Linghua Tang, Yaqi Shen, Jin Xie, and Jian Yang. Learning superpoint graph cut for 3D instance segmentation. NIPS, 35:36804–36817, 2022. 1, 2, 6, 7

2022
[14]

Pointgroup: Dual-set point grouping for 3D instance segmentation

Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi- Wing Fu, and Jiaya Jia. Pointgroup: Dual-set point grouping for 3D instance segmentation. In CVPR, pages 4867–4876,
[15]

Mask-free video instance seg- mentation

Lei Ke, Martin Danelljan, Henghui Ding, Yu-Wing Tai, Chi- Keung Tang, and Fisher Yu. Mask-free video instance seg- mentation. In CVPR, pages 22857–22866, 2023. 3

2023
[16]

Segment any- thing

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In ICCV, pages 4015–4026, 2023. 2

2023
[17]

OneFormer3D: One transformer for unified point cloud segmentation

Maxim Kolodiazhnyi, Anna V orontsova, Anton Konushin, and Danila Rukhovich. OneFormer3D: One transformer for unified point cloud segmentation. In CVPR, pages 20943– 20953, 2024. 2

2024
[18]

The hungarian method for the assignment problem

Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83–97,
[19]

Mask-Attention-Free Transformer for 3D in- stance segmentation

Xin Lai, Yuhui Yuan, Ruihang Chu, Yukang Chen, Han Hu, and Jiaya Jia. Mask-Attention-Free Transformer for 3D in- stance segmentation. In ICCV, pages 3693–3703, 2023. 1, 2

2023
[20]

DiscoBox: Weakly supervised instance segmen- tation and semantic correspondence from box supervision

Shiyi Lan, Zhiding Yu, Christopher Choy, Subhashree Rad- hakrishnan, Guilin Liu, Yuke Zhu, Larry S Davis, and Anima Anandkumar. DiscoBox: Weakly supervised instance segmen- tation and semantic correspondence from box supervision. In ICCV, pages 3406–3416, 2021. 3

2021
[21]

Large-scale point cloud semantic segmentation with superpoint graphs

Loic Landrieu and Martin Simonovsky. Large-scale point cloud semantic segmentation with superpoint graphs. In CVPR, pages 4558–4567, 2018. 3

2018
[22]

Instance segmentation in 3D scenes using semantic superpoint tree networks

Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, and Kui Jia. Instance segmentation in 3D scenes using semantic superpoint tree networks. In ICCV, pages 2783–2792, 2021. 2

2021
[23]

Point cloud instance segmen- tation with semi-supervised bounding-box mining

Yongbin Liao, Hongyuan Zhu, Yanggang Zhang, Chuangguan Ye, Tao Chen, and Jiayuan Fan. Point cloud instance segmen- tation with semi-supervised bounding-box mining. TPAMI, 44(12):10159–10170, 2021. 1, 2

2021
[24]

One thing one click: A self-training approach for weakly supervised 3D semantic segmentation

Zhengzhe Liu, Xiaojuan Qi, and Chi-Wing Fu. One thing one click: A self-training approach for weakly supervised 3D semantic segmentation. In CVPR, pages 1726–1736, 2021. 1, 2

2021
[25]

Group-free 3D object detection via transformers

Ze Liu, Zheng Zhang, Yue Cao, Han Hu, and Xin Tong. Group-free 3D object detection via transformers. In ICCV, pages 2949–2958, 2021. 1

2021
[26]

Query refinement transformer for 3D instance segmentation

Jiahao Lu, Jiacheng Deng, Chuxin Wang, Jianfeng He, and Tianzhu Zhang. Query refinement transformer for 3D instance segmentation. In ICCV, pages 18516–18526, 2023. 2

2023
[27]

BSNet: Box- supervised simulation-assisted mean teacher for 3D instance segmentation

Jiahao Lu, Jiacheng Deng, and Tianzhu Zhang. BSNet: Box- supervised simulation-assisted mean teacher for 3D instance segmentation. In CVPR, pages 20374–20384, 2024. 2, 3, 6, 7

2024
[28]

An end- to-end transformer model for 3D object detection

Ishan Misra, Rohit Girdhar, and Armand Joulin. An end- to-end transformer model for 3D object detection. In ICCV, pages 2906–2917, 2021. 1

2021
[29]

GaPro: Box-supervised 3D point cloud instance segmentation using gaussian processes as pseudo labelers

Tuan Duc Ngo, Binh-Son Hua, and Khoi Nguyen. GaPro: Box-supervised 3D point cloud instance segmentation using gaussian processes as pseudo labelers. In ICCV, pages 17794– 17803, 2023. 1, 2, 3, 6, 7

2023
[30]

ISBNet: a 3D point cloud instance segmentation network with instance- aware sampling and box-aware dynamic convolution

Tuan Duc Ngo, Binh-Son Hua, and Khoi Nguyen. ISBNet: a 3D point cloud instance segmentation network with instance- aware sampling and box-aware dynamic convolution. In CVPR, pages 13550–13559, 2023. 1, 2, 6, 7

2023
[31]

Deep hough voting for 3D object detection in point clouds

Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3D object detection in point clouds. In ICCV, pages 9277–9286, 2019. 1

2019
[32]

UnScene3D: Unsupervised 3D instance segmentation for indoor scenes

David Rozenberszki, Or Litany, and Angela Dai. UnScene3D: Unsupervised 3D instance segmentation for indoor scenes. In CVPR, pages 19957–19967, 2024. 7

2024
[33]

FCAF3D: Fully convolutional anchor-free 3D object detec- tion

Danila Rukhovich, Anna V orontsova, and Anton Konushin. FCAF3D: Fully convolutional anchor-free 3D object detec- tion. In ECCV, pages 477–493. Springer, 2022. 1

2022
[34]

Mask3D: Mask trans- former for 3D semantic instance segmentation

Jonas Schult, Francis Engelmann, Alexander Hermans, Or Litany, Siyu Tang, and Bastian Leibe. Mask3D: Mask trans- former for 3D semantic instance segmentation. In ICRA, pages 8216–8223. IEEE, 2023. 1, 2, 6, 7

2023
[35]

Spherical mask: Coarse-to- fine 3D point cloud instance segmentation with spherical representation

Sangyun Shin, Kaichen Zhou, Madhu Vankadari, Andrew Markham, and Niki Trigoni. Spherical mask: Coarse-to- fine 3D point cloud instance segmentation with spherical representation. In CVPR, pages 4060–4069, 2024. 6, 7

2024
[36]

Superpoint transformer for 3D scene instance segmentation

Jiahao Sun, Chunmei Qing, Junpeng Tan, and Xiangmin Xu. Superpoint transformer for 3D scene instance segmentation. In AAAI, pages 2393–2401, 2023. 1, 2, 6

2023
[37]

Learning inter-superpoint affinity for weakly supervised 3D instance segmentation

Linghua Tang, Le Hui, and Jin Xie. Learning inter-superpoint affinity for weakly supervised 3D instance segmentation. In ACCV, pages 1282–1297, 2022. 1, 2

2022
[38]

Seg- Group: Seg-level supervision for 3D instance and semantic segmentation

An Tao, Yueqi Duan, Yi Wei, Jiwen Lu, and Jie Zhou. Seg- Group: Seg-level supervision for 3D instance and semantic segmentation. TIP, 31:4952–4965, 2022. 1, 2

2022
[39]

BoxInst: High-performance instance segmentation with box annotations

Zhi Tian, Chunhua Shen, Xinlong Wang, and Hao Chen. BoxInst: High-performance instance segmentation with box annotations. In CVPR, pages 5443–5452, 2021. 3

2021
[40]

MSTA3D: Multi-scale twin-attention for 3d instance segmen- tation

Duc Dang Trung Tran, Byeongkeun Kang, and Yeejin Lee. MSTA3D: Multi-scale twin-attention for 3d instance segmen- tation. In ACM MM, pages 1467–1475, 2024. 2, 6, 7

2024
[41]

SoftGroup for 3D instance segmentation on point clouds

Thang Vu, Kookhoi Kim, Tung M Luu, Thanh Nguyen, and Chang D Yoo. SoftGroup for 3D instance segmentation on point clouds. In CVPR, pages 2708–2717, 2022. 1, 2, 7

2022
[42]

CAGroup3D: Class-aware grouping for 3D object detection on point clouds

Haiyang Wang, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, Liwei Wang, et al. CAGroup3D: Class-aware grouping for 3D object detection on point clouds. NIPS, 35:29975–29988, 2022. 1

2022
[43]

RBGNet: Ray-based grouping for 3D object detection

Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, and Liwei Wang. RBGNet: Ray-based grouping for 3D object detection. In CVPR, pages 1110–1119, 2022

2022
[44]

MLCVNet: Multi-level context votenet for 3D object detection

Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Yiming Zhang, Kai Xu, and Jun Wang. MLCVNet: Multi-level context votenet for 3D object detection. In CVPR, pages 10447–10456, 2020. 1

2020
[45]

PointContrast: Unsupervised pre- training for 3D point cloud understanding

Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas Guibas, and Or Litany. PointContrast: Unsupervised pre- training for 3D point cloud understanding. In ECCV, pages 574–591. Springer, 2020. 1, 2

2020
[46]

Learning object bounding boxes for 3D instance segmentation on point clouds

Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, and Niki Trigoni. Learning object bounding boxes for 3D instance segmentation on point clouds. NIPS, 32, 2019. 2

2019
[47]

2D-3D interlaced transformer for point cloud segmentation with scene-level supervision

Cheng-Kun Yang, Min-Hung Chen, Yung-Yu Chuang, and Yen-Yu Lin. 2D-3D interlaced transformer for point cloud segmentation with scene-level supervision. In ICCV, pages 977–987, 2023. 2

2023
[48]

GSPN: Generative shape proposal network for 3D instance segmentation in point cloud

Li Yi, Wang Zhao, He Wang, Minhyuk Sung, and Leonidas J Guibas. GSPN: Generative shape proposal network for 3D instance segmentation in point cloud. In CVPR, pages 3947– 3956, 2019. 2

2019
[49]

When 3D bounding-box meets SAM: Point cloud instance segmentation with weak-and-noisy supervision

Qingtao Yu, Heming Du, Chen Liu, and Xin Yu. When 3D bounding-box meets SAM: Point cloud instance segmentation with weak-and-noisy supervision. In WACV, pages 3719– 3728, 2024. 1, 2, 3, 6

2024
[50]

H3DNet: 3D object detection using hybrid geometric primi- tives

Zaiwei Zhang, Bo Sun, Haitao Yang, and Qixing Huang. H3DNet: 3D object detection using hybrid geometric primi- tives. In ECCV, pages 311–329. Springer, 2020. 1

2020
[51]

Hy- perDet3D: Learning a scene-conditioned 3D object detector

Yu Zheng, Yueqi Duan, Jiwen Lu, Jie Zhou, and Qi Tian. Hy- perDet3D: Learning a scene-conditioned 3D object detector. In CVPR, pages 5585–5594, 2022. 1

2022
[52]

Deformable DETR: Deformable trans- formers for end-to-end object detection

Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable DETR: Deformable trans- formers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. 2

2010 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[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 CVPR, pages 1534– 1543, 2016. 6

work page 2016

[2] [2]

Hierarchical aggregation for 3D instance segmentation

Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, and Xinggang Wang. Hierarchical aggregation for 3D instance segmentation. In CVPR, pages 15467–15476, 2021. 1, 2

work page 2021

[3] [3]

Masked-attention mask trans- former for universal image segmentation

Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar. Masked-attention mask trans- former for universal image segmentation. In CVPR, pages 1290–1299, 2022. 2

work page 2022

[4] [4]

Box2Mask: Weakly supervised 3D semantic instance segmentation using bounding boxes

Julian Chibane, Francis Engelmann, Tuan Anh Tran, and Ger- ard Pons-Moll. Box2Mask: Weakly supervised 3D semantic instance segmentation using bounding boxes. InECCV, pages 681–699. Springer, 2022. 1, 2, 3, 6, 7

work page 2022

[5] [5]

Twist: Two-way inter-label self-training for semi-supervised 3D instance seg- mentation

Ruihang Chu, Xiaoqing Ye, Zhengzhe Liu, Xiao Tan, Xi- aojuan Qi, Chi-Wing Fu, and Jiaya Jia. Twist: Two-way inter-label self-training for semi-supervised 3D instance seg- mentation. In CVPR, pages 1100–1109, 2022. 1, 2

work page 2022

[6] [6]

ScanNet: Richly- annotated 3D reconstructions of indoor scenes

Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly- annotated 3D reconstructions of indoor scenes. In CVPR, pages 5828–5839, 2017. 1, 6

work page 2017

[7] [7]

Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision

Shichao Dong, Ruibo Li, Jiacheng Wei, Fayao Liu, and Gu- osheng Lin. Rwseg: Cross-graph competing random walks for weakly supervised 3D instance segmentation. arXiv preprint arXiv:2208.05110, 2022. 1, 2

work page Pith review arXiv 2022

[8] [8]

Weakly-supervised point cloud instance segmentation with geometric priors

Heming Du, Xin Yu, Farookh Hussain, Mohammad Ali Armin, Lars Petersson, and Weihao Li. Weakly-supervised point cloud instance segmentation with geometric priors. In WACV, pages 4271–4280, 2023. 1, 2, 3, 6, 7

work page 2023