MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing | Paper Citation Record

Observation 30a368b9-689a-472a-bdb4-643b95d5bdbb · outbound

This paper cites Visual prompting via image inpaint- ing.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Visual prompting via image inpaint- ing

Reference 1

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 23b2c053-d31f-429c-9378-4f3a45cd0f9e · outbound

This paper cites Adaptive neural networks for efficient inference.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Adaptive neural networks for efficient inference

Reference 2

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Observation c0efa6f4-d577-47b7-9662-0b4ee7b368b6 · outbound

This paper cites Lan- guage models are few-shot learners.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Lan- guage models are few-shot learners

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.731978Z

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Observation 7305c5e0-3bf0-4c69-a34f-7f694bb09e12 · outbound

This paper cites From ranknet to lambdarank to lambdamart: An overview.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing From ranknet to lambdarank to lambdamart: An overview

Reference 4

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Observation 31e35e9b-0ae0-4737-a322-f53e88f8b10d · outbound

This paper cites Shapenet: An information-rich 3d model repository.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Shapenet: An information-rich 3d model repository

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.714045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ce4c041e-6a4f-4464-9838-e726052c22e9 · outbound

This paper cites Unsu- pervised learning of geometric sampling invariant represen- tations for 3d point clouds.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Unsu- pervised learning of geometric sampling invariant represen- tations for 3d point clouds

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.704887Z

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Observation bf33a64e-536f-44c8-b9ec-37678a2d8702 · outbound

This paper cites Pra-net: Point relation-aware network for 3d point cloud analysis.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pra-net: Point relation-aware network for 3d point cloud analysis

Reference 7

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b0dd01e-194c-4c10-88b1-27e51e8b56f5 · outbound

This paper cites Autoencoders as cross-modal teachers: Can pretrained 2d image transform- ers help 3d representation learning? In ICLR, 2023.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Autoencoders as cross-modal teachers: Can pretrained 2d image transform- ers help 3d representation learning? In ICLR, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.686612Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fdc65eb5-90fe-47fd-b31d-ccb2aa9046c7 · outbound

This paper cites Learning to sam- ple.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Learning to sam- ple

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a37e2665-bab1-4b09-ac86-aadf5944ec6f · outbound

This paper cites A point set generation network for 3d object reconstruction from a single image.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing A point set generation network for 3d object reconstruction from a single image

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.668787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8fa478a0-6b12-42f0-b135-364cbae80ee6 · outbound

This paper cites Explore in-context learning for 3d point cloud understanding.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Explore in-context learning for 3d point cloud understanding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.659482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec41573a-80bd-49e1-bfff-de6103418b8b · outbound

This paper cites Discriminatory analysis: nonparametric dis- crimination, consistency properties.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Discriminatory analysis: nonparametric dis- crimination, consistency properties

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.650516Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cfa6648f-628b-4dc4-9911-763c1e5ef2d7 · outbound

This paper cites Flex-convolution: Million-scale point-cloud learning beyond grid-worlds.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Flex-convolution: Million-scale point-cloud learning beyond grid-worlds

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.641367Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1375e41c-0ce6-4e12-b276-5d5c022e5404 · outbound

This paper cites Pct: Point cloud transformer.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pct: Point cloud transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.632649Z

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Observation c42e3d4c-4679-49c7-b7ef-9aff57d943c2 · outbound

This paper cites Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

Reference 15

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Observation b2b32591-e9f7-4f39-9ed9-1867b4925375 · outbound

This paper cites Segpoint: Segment any point cloud via large language model.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Segpoint: Segment any point cloud via large language model

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.623845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 832a6239-90a9-4568-9238-8caf6d2d431d · outbound

This paper cites Randla-net: Efficient semantic segmentation of large-scale point clouds.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Randla-net: Efficient semantic segmentation of large-scale point clouds

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.614239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d45711d6-a2b7-4649-8b38-ae62941577ad · outbound

This paper cites Surface reconstruction from point clouds: A survey and a benchmark.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Surface reconstruction from point clouds: A survey and a benchmark

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.605322Z

Source-reported events for the cited work

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Observation f2fe2dc2-a993-497b-a46b-03ff791f0617 · outbound

This paper cites Categorical repa- rameterization with gumbel-softmax.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Categorical repa- rameterization with gumbel-softmax

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.596330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 936bd362-1a10-4caf-aca3-6b1c3aefa7ca · outbound

This paper cites Subjective and objective quality evaluation of 3d point cloud denoising algorithms.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Subjective and objective quality evaluation of 3d point cloud denoising algorithms

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.586175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0db2b779-92ef-4804-8e63-b95ebd49b8d4 · outbound

This paper cites Bayesian point cloud re- construction.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Bayesian point cloud re- construction

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.576813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d8a134c4-ed0c-463d-8137-c15940afbc2a · outbound

This paper cites Dg- pic: Domain generalized point-in-context learning for point cloud understanding.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Dg- pic: Domain generalized point-in-context learning for point cloud understanding

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.567642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 43b2b95a-3dd6-4172-abed-3d775b437f19 · outbound

This paper cites Normalization matters in weakly supervised object localiza- tion.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Normalization matters in weakly supervised object localiza- tion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.558653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4f2cd2e8-e729-417b-ad7f-e9b6d4be482e · outbound

This paper cites Oneformer3d: One transformer for unified point cloud segmentation.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Oneformer3d: One transformer for unified point cloud segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.549823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2dafc780-52ed-4eb8-94f2-56f1520c892c · outbound

This paper cites Large-scale point cloud semantic segmentation with superpoint graphs.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Large-scale point cloud semantic segmentation with superpoint graphs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.540582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1c5f92ce-87ef-4cde-a9d8-d58c82bbb889 · outbound

This paper cites Samplenet: Differ- entiable point cloud sampling.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Samplenet: Differ- entiable point cloud sampling

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.531625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ef571cea-3def-45f5-b4f8-e05dffe966b4 · outbound

This paper cites Diverse demon- strations improve in-context compositional generalization.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Diverse demon- strations improve in-context compositional generalization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.522591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a68a9d49-a543-47fb-a987-fc4fbaa1b46b · outbound

This paper cites Uni- fied demonstration retriever for in-context learning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Uni- fied demonstration retriever for in-context learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.512883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eb901252-77e5-47d0-a84e-cb6f11a05673 · outbound

This paper cites Pointcnn: Convolution on x-transformed points.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pointcnn: Convolution on x-transformed points

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.502478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c6b1542f-75f6-4168-ba23-d019e4a13118 · outbound

This paper cites What makes good in- context examples for gpt-3? In DeeLIO, 2022.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing What makes good in- context examples for gpt-3? In DeeLIO, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.493374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 790989d9-3bfc-489a-9156-1e22e3f8b99a · outbound

This paper cites Point-in-context: Understanding point cloud via in-context learning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Point-in-context: Understanding point cloud via in-context learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.484271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a74a18ab-1251-46a1-91fc-38f9ac7332c2 · outbound

This paper cites Relation-shape convolutional neural network for point cloud analysis.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Relation-shape convolutional neural network for point cloud analysis

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.475123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b1043c1a-3a4d-4766-860b-a94522d97a72 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Sgdr: Stochastic gradient descent with warm restarts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.465633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b9b74a1-d05c-4790-b81e-a109cbebf0e3 · outbound

This paper cites Score-based point cloud denoising.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Score-based point cloud denoising

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.455091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1345adc3-daa7-44a9-ab73-cf52cc1cde75 · outbound

This paper cites Large language model and domain- specific model collaboration for smart education.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Large language model and domain- specific model collaboration for smart education

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.445705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 857b18b9-2a93-417c-8b18-adf6374ffd3b · outbound

This paper cites Re- thinking network design and local geometry in point cloud: A simple residual mlp framework.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Re- thinking network design and local geometry in point cloud: A simple residual mlp framework

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.436141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53035c50-7da0-4541-a81a-3e4dc15b3428 · outbound

This paper cites Dense 3d point cloud reconstruction using a deep pyramid network.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Dense 3d point cloud reconstruction using a deep pyramid network

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.426253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 915495c9-202b-4bfc-ad56-5c14b41d4865 · outbound

This paper cites Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.874118Z

Source-reported events for the cited work

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Observation 187e5de6-2e5f-4bf5-a793-930a0fd62422 · outbound

This paper cites Adaptive hierarchical down-sampling for point cloud classification.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Adaptive hierarchical down-sampling for point cloud classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.416480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f90690d-f1e0-482e-ab29-d064ea72c4b3 · outbound

This paper cites Point-E: A System for Generating 3D Point Clouds from Complex Prompts.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Point-E: A System for Generating 3D Point Clouds from Complex Prompts

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.880859Z

Source-reported events for the cited work

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Observation 0b1941b6-d37b-4924-b2bf-2cd692bcd8ea · outbound

This paper cites Cross-lingual retrieval augmented prompt for low- resource languages.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Cross-lingual retrieval augmented prompt for low- resource languages

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.406648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9627e42-3ca0-4e07-8a5f-e52659a7ead7 · outbound

This paper cites Masked autoencoders for point cloud self-supervised learning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Masked autoencoders for point cloud self-supervised learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.396956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f8293ce-e568-47f6-a074-eba7a89ffdf7 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.386352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 94f9bdc5-5126-4169-8217-297d16c19cb8 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.376849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d432de12-d5d6-4e21-9d11-45a377388d1e · outbound

This paper cites Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.367337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 566876ee-2402-4a58-8210-354486375d2e · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Learn- ing transferable visual models from natural language super- vision

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.900994Z

Source-reported events for the cited work

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Observation ab8deb15-a2d1-487e-9b03-978ed552473c · outbound

This paper cites Learn- ing to retrieve prompts for in-context learning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Learn- ing to retrieve prompts for in-context learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.351978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d355f445-135c-4b1e-a05e-6b55db408cd1 · outbound

This paper cites Gpa-net: No-reference point cloud quality assessment with multi-task graph convolu- tional network.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Gpa-net: No-reference point cloud quality assessment with multi-task graph convolu- tional network

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.342055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc9087ea-db5a-4530-9335-3106d55240ab · outbound

This paper cites Active learning for point cloud semantic segmentation via spatial-structural diversity reasoning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Active learning for point cloud semantic segmentation via spatial-structural diversity reasoning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.332596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1dd13217-133c-4c69-88e8-844b1e525dc8 · outbound

This paper cites Minigpt-3d: Efficiently aligning 3d point clouds with large language models using 2d priors.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Minigpt-3d: Efficiently aligning 3d point clouds with large language models using 2d priors

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.322998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 87400125-3268-4f00-bb35-7797fc039036 · outbound

This paper cites Kpconv: Flexible and deformable convolution for point clouds.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Kpconv: Flexible and deformable convolution for point clouds

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.312763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a0d89dc5-6dc8-4227-9f5b-107026cb4881 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing LLaMA: Open and Efficient Foundation Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.920739Z

Source-reported events for the cited work

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Observation b1463cd0-a73f-4c81-aa12-2f3d77d8f5c5 · outbound

This paper cites Assessing normalization techniques for simple ad- ditive weighting method.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Assessing normalization techniques for simple ad- ditive weighting method

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.303130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 097c2f4a-a83f-494c-a8bf-1be17f70ae75 · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Images speak in images: A generalist painter for in-context visual learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.293966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4368d67c-7887-4b57-9f75-3e81f8437640 · outbound

This paper cites Seggpt: Segmenting ev- erything in context.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Seggpt: Segmenting ev- erything in context

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.284626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b4f62f71-3944-47af-8483-85e9ef0e994f · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Dynamic graph cnn for learning on point clouds

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.274443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cb48ae65-df4c-4b4f-a58a-be26313a335d · outbound

This paper cites Pathnet: Path-selective point cloud denoising.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pathnet: Path-selective point cloud denoising

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.264500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 564aa449-a82f-4758-b778-a4d09b2ff33d · outbound

This paper cites Learnable skeleton-aware 3d point cloud sampling.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Learnable skeleton-aware 3d point cloud sampling

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.255291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9879b441-9111-456e-b880-7f27a9422530 · outbound

This paper cites Attention-based point cloud edge sampling.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Attention-based point cloud edge sampling

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.246189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d5965b0-0fb2-4e66-af86-c3a1f42f3f58 · outbound

This paper cites Indexsample: A learnable sampling network in point cloud classification.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Indexsample: A learnable sampling network in point cloud classification

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.235982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation acef0b4d-8350-4453-a3cb-78527384e4a2 · outbound

This paper cites Pointllm: Empowering large lan- guage models to understand point clouds.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pointllm: Empowering large lan- guage models to understand point clouds

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.226145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7ace59bd-bd9a-4c4f-ac9a-c6b5a9fdddda · outbound

This paper cites In-context learning with retrieved demonstrations for lan- guage models: A survey.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing In-context learning with retrieved demonstrations for lan- guage models: A survey

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.216816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 77533289-e119-499c-bb86-1f64e141ce6a · outbound

This paper cites Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.206217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 85a6f3c0-5a3e-4d26-abbc-cf69979be549 · outbound

This paper cites Teaser: Fast and certifiable point cloud registration.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Teaser: Fast and certifiable point cloud registration

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.195860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b5432a2a-592c-438f-a5fc-82e78e28eb7a · outbound

This paper cites Modeling point clouds with self-attention and gumbel subset sampling.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Modeling point clouds with self-attention and gumbel subset sampling

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.186175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a885e2cf-0abb-45a3-8045-86855dcb7271 · outbound

This paper cites A scalable active framework for region annotation in 3d shape collections.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing A scalable active framework for region annotation in 3d shape collections

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.965569Z

Source-reported events for the cited work

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Observation 3dedfdde-2c4a-47bd-b306-eb040460a1fa · outbound

This paper cites Generate rather than retrieve: Large language models are strong context generators.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Generate rather than retrieve: Large language models are strong context generators

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.171211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39eaad77-15cb-4c87-ab3d-e6ed2312a923 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.161391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 21c61a11-d7c1-4846-b749-754350530404 · outbound

This paper cites Clip2: Contrastive language- image-point pretraining from real-world point cloud data.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Clip2: Contrastive language- image-point pretraining from real-world point cloud data

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.152265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d4334b9d-1cce-4010-9577-e80f75545e0b · outbound

This paper cites Pointclip: Point cloud understanding by clip.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Pointclip: Point cloud understanding by clip

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.142568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d32e588c-1f9e-449a-a51c-a7e8458ec21c · outbound

This paper cites Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.982765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e632e1ca-4387-499c-b680-e786e49c103f · outbound

This paper cites A survey on multi-task learning.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing A survey on multi-task learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.126483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a29d677-1803-41c8-b785-86107b8fb996 · outbound

This paper cites Svc: Sight view constraint for robust point cloud registration.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Svc: Sight view constraint for robust point cloud registration

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.116368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2347f71c-8544-4a74-bca9-8031eab08f43 · outbound

This paper cites Automatic chain of thought prompting in large language models.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Automatic chain of thought prompting in large language models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T13:35:40.992492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1a7c959-c9e8-47f1-bcac-482a95b6da2b · outbound

This paper cites Robust multi-task learning network for com- plex lidar point cloud data preprocessing.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Robust multi-task learning network for com- plex lidar point cloud data preprocessing

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.101638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 89138cce-c517-4564-a6fc-b3685f39b656 · outbound

This paper cites an unresolved cited work.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:35:41.091962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 398372d0-6f42-40b1-8ff9-c6c77756fd0e · outbound

This paper cites As shown in Figures A1 and A2, our proposed MICAS consistently selects higher-quality central points, delivering superior outcomes and overcom- ing the limitations of FPS.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing As shown in Figures A1 and A2, our proposed MICAS consistently selects higher-quality central points, delivering superior outcomes and overcom- ing the limitations of FPS

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:35:41.082377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9dcca39-abc6-445a-97ea-fc5697307faf · outbound

This paper cites best-performing.

MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing best-performing

Reference 78

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T13:35:41.071522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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