LangTail uses entity-level semantic priors from language models aligned via contrastive learning in a hierarchical clustering setup to resolve long-tail ambiguity, yielding +13.5, +12.9, and +8.9 mIoU gains on ScanNet-v2, S3DIS, and nuScenes.
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Pointnet: Deep learning on point sets for 3d classification and segmentation
12 Pith papers cite this work. Polarity classification is still indexing.
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DockAnywhere lifts single demonstrations to diverse docking points via structure-preserving augmentation and point-cloud spatial editing to improve viewpoint generalization in visuomotor policies for mobile manipulation.
CoLA-Flow Policy encodes action sequences into a continuous latent space and learns an explicit flow there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher task success than raw-action flow baselines.
Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.
HITL-D combines diffusion policies with human input for shared robotic control, reducing required joystick axes and improving speed and workload in manipulation tasks per a 12-participant study.
Real2Sim reconstructs editable dynamic driving scenes as temporally continuous Gaussians integrated with a differentiable MPM physics solver for high-fidelity simulation of interactions and collisions.
A modular belief-space controller using learned Belief Control Lyapunov Functions for information gathering and conformal-prediction Belief Control Barrier Functions for safety reduces reach-avoid POMDP synthesis to fast quadratic programs.
MapRF reaches about 75% of fully supervised HD map accuracy on Argoverse 2 and nuScenes by generating view-consistent pseudo labels via a NeRF conditioned on map predictions and refining them with Map-to-Ray Matching in self-training.
PointCRA reduces information loss in deep point cloud networks by treating temporal trend variation as an extra evaluation dimension alongside spatial and channel attention, guided by a neighborhood homogeneity constraint.
FastGrasp uses two-stage RL with CVAE for diverse grasp candidates from point clouds and tactile sensing for impact adjustments to achieve robust fast whole-body grasping in sim and real-world settings.
Empowered t-FCW graph representation provides a unified non-parametric and interpretable method for point cloud analysis with high efficiency on ModelNet40 classification.
FP16 quantization preserves accuracy in BEV-based LiDAR place recognition at lower cost while INT8 degradation depends on the network architecture.
citing papers explorer
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Geometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation
Proposes the first light field-LiDAR semantic segmentation dataset and the Mlpfseg network, which improves mIoU by 1.71 over image-only and 2.38 over point-cloud-only baselines via feature completion and depth perception modules.
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MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
MapRF reaches about 75% of fully supervised HD map accuracy on Argoverse 2 and nuScenes by generating view-consistent pseudo labels via a NeRF conditioned on map predictions and refining them with Map-to-Ray Matching in self-training.