{"total":10,"items":[{"citing_arxiv_id":"2605.17354","ref_index":33,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"GeoHand: Unlocking Prior Geometry Knowledge for Monocular 3D Hand Reconstruction","primary_cat":"cs.CV","submitted_at":"2026-05-17T09:45:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GeoHand adapts priors from a general-scene geometry estimator via a GeoAdapter, gated fusion, and keypoint-queried refiner to reach SOTA monocular 3D hand reconstruction on FreiHAND, DexYCB, and HO3Dv3 under heavy occlusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15923","ref_index":43,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction","primary_cat":"cs.CV","submitted_at":"2026-05-15T13:06:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Invaria trains point cloud encoders with next-resolution prediction to learn scale and density invariant features, yielding higher mIoU on ScanNet under lower resolution and scaled objects while using a smaller model.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09196","ref_index":30,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"RigidFormer: Learning Rigid Dynamics using Transformers","primary_cat":"cs.CV","submitted_at":"2026-05-09T22:31:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RigidFormer learns mesh-free rigid dynamics from point clouds using object-centric anchors, Anchor-Vertex Pooling, Anchor-based RoPE, and differentiable Kabsch alignment to enforce rigidity.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"v ×3 (used as a discrete velocity surrogate), (3) the reference-offset r(i) t =x (i) t −x (i) ref ∈R N (i) v ×3 where the reference is the first frame in the sequence, and (4) physics parameters ϕ(i) = [m, µ, ϵ]∈R 3 (mass, friction, restitution), broadcast to every vertex of object i. These yield the input feature h(i) t ∈R N (i) v ×12. Inspired by PointNet [ 30], we build an encoder Encθ with hierarchical feature extraction to aggregate per-vertex features into a fixed-dimensional object embedding: o(i) t = Encθ(h(i) t )∈R D. After computing per-vertex features, we extract multi-scale geometry at the global level and three subsampled levels; these features are then concatenated and fused into one object-level embedding."},{"citing_arxiv_id":"2605.08436","ref_index":3,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds","primary_cat":"cs.LG","submitted_at":"2026-05-08T19:59:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"MEEC equips point clouds with a discrete exterior calculus that satisfies exact conservation and is differentiable in point positions, allowing a single trained kernel to produce compatible physics on unseen geometries and parameters.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"nonlinear operators via deeponet based on the universal approximation theorem of operators. Nature machine intelligence, 3(3):218-229, 2021. [2] Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Neural operator: Graph kernel network for partial differential equations.arXiv preprint arXiv:2003.03485, 2020. [3] 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, 2017. [4] Ali Kashefi, Davis Rempe, and Leonidas J Guibas. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries."},{"citing_arxiv_id":"2605.08377","ref_index":9,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling","primary_cat":"cs.LG","submitted_at":"2026-05-08T18:34:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"New lower bounds establish that Deep Sets need embedding dimension linear in the number of points (up to constants) for d>1, and give the first non-trivial bounds for higher-order Janossy pooling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.23629","ref_index":52,"ref_count":2,"confidence":0.55,"is_internal_anchor":false,"paper_title":"From Visual Synthesis to Interactive Worlds: Toward Production-Ready 3D Asset Generation","primary_cat":"cs.GR","submitted_at":"2026-04-26T09:44:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"procedural content generation. InFindings of the Association for Computational Linguistics: ACL 2025, pages 19417-19435, 2025. doi: 10.18653/v1/2025.findings-acl.994. [51] Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. Deep learning for 3d point clouds: A survey.IEEE Trans. Pattern Anal. Mach. Intell., 43(12):4338-4364, 2020. [52] 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, 2017. [53] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes."},{"citing_arxiv_id":"2604.05828","ref_index":62,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Precise Aggressive Aerial Maneuvers with Sensorimotor Policies","primary_cat":"cs.RO","submitted_at":"2026-04-07T13:00:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Reinforcement learning sensorimotor policies enable quadrotors to traverse narrow gaps at extreme tilts with 5 cm clearance using only vision and proprioception, including reactive traversal of moving gaps.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02446","ref_index":6,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"From Elevation Maps To Contour Lines: SVM and Decision Trees to Detect Violin Width Reduction","primary_cat":"cs.CV","submitted_at":"2026-04-02T18:16:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Contour line features from 3D violin meshes outperform or match raw elevation maps when using SVM and decision trees to detect width reductions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.19830","ref_index":7,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Real-Time Structural Detection for Indoor Navigation from 3D LiDAR Using Bird's-Eye-View Images","primary_cat":"cs.RO","submitted_at":"2026-03-20T10:15:49+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Projecting 3D LiDAR to BEV images and applying YOLO-OBB with spatiotemporal fusion enables reliable real-time structural detection on resource-constrained robots.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.23634","ref_index":2,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Monotone and Separable Set Functions: Characterizations and Neural Models","primary_cat":"cs.LG","submitted_at":"2025-10-24T09:59:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Characterizes monotone separating set functions with dimension bounds, proves non-existence on infinite domains, and introduces a Holder-stable neural model with a weak version of the property for universal monotone approximation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}