3d mapping engineers

Abstract: Point cloud registration is an essential task for scene reconstruction, requiring the alignment of partially overlapping point clouds. Under low-overlap conditions, this task remains challenging because existing geometry-only methods cannot distinguish geometrically similar but semantically distinct structures (e.g., TV corners vs. desk corners and chair legs vs. table legs), resulting in incorrect correspondences. To address this geometric ambiguity, we present Semantic-Augmented Geometric Experts (SAGE), which integrates frozen pre-trained point cloud semantic representations into the registration pipeline without requiring additional sensors or fine-tuning the semantic encoder. To effectively leverage these semantic distinctions, SAGE employs a Semantic-Guided Mixture of Experts (SG-MoE) architecture. Through semantic-aware routing, SG-MoE enables each expert to specialize in distinct semantic-geometric patterns, reducing mismatches that confound geometry-only methods. With a structure-aware estimator, SAGE achieves the highest registration recall on 3DLoMatch while remaining competitive on 3DMatch. When evaluated with the same number of refinement iterations, SAGE further achieves the highest inlier ratio and registration recall on both benchmarks. These results indicate that frozen semantic representations provide effective cues for resolving geometric ambiguity in low-overlap registration.

Abstract: Cross-attention is a crucial component in learning-based image-to-point-cloud (I2P) registration. Although existing cross-attention mechanisms have achieved promising progress, attention ambiguity remains a fundamental challenge that hinders the learning of discriminative 2D-3D correspondences. To address this problem, we revisit cross-attention and establish ordinary differential equations (ODEs) to model the ideal I2P feature interaction. Based on this formulation, we develop an ODE-driven cross-attention (OCA) module that refines feature representations and attention matrices through ODEs. In practice, OCA can be seamlessly integrated into existing I2P registration frameworks. To validate its effectiveness, we incorporate OCA into five state-of-the-art baselines and evaluate on four public benchmark datasets. Experimental results demonstrate that OCA improves registration recall by up to 5\%, 9\%, and 15\% under the standard, fine-tuning, and zero-shot settings, respectively.

Abstract: Open-vocabulary scene understanding is fundamental for robotics, laying the groundwork for spatial reasoning and object manipulation. While closed-vocabulary 3D instance segmentation heavily leverages 3D shape information, state-of-the-art open-vocabulary methods remain predominantly restricted to 2D image features or image-distilled representations during mask labeling. In this paper, we propose SenseFuse, a label-free fusion method that balances 2D image and 3D shape encoders for robust open-vocabulary 3D instance segmentation, refining only the mask-labeling stage of existing pipelines. We reveal that 2D image and 3D shape encoders exhibit largely disjoint failure patterns and rarely share identical wrong labels, whereas two 2D image encoders frequently repeat the same errors. This distinct behavior makes the 2D and 3D pair inherently complementary. We introduce an adaptive mechanism that selects a scene-level fusion weight to maximize a label-free sensitivity measure, estimated directly from a single scene's unlabeled proposals in milliseconds. SenseFuse improves labeling accuracy in every evaluated setting across ScanNet200, Replica, and ScanNet++, recovering 67-100% (median 93%) of the gain achievable with an oracle weight, and it raises instance AP in 21 of 22 reported settings. Code is available at https://github.com/hanes1207/SenseFuse.