DENALI is the first large-scale real-world dataset of space-time histograms from low-cost LiDARs for training models to perceive hidden objects via multi-bounce light cues.
Se- mantickitti: A dataset for semantic scene understanding of lidar sequences
6 Pith papers cite this work. Polarity classification is still indexing.
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GIBLy is an architecture-agnostic lightweight layer that adds learnable geometric priors aligned with simple shapes to 3D semantic segmentation pipelines, delivering consistent mIoU gains (up to +11.5% on TS40K) with only 58K extra parameters.
UniD-Shift decomposes 2D and 3D features into shared semantic and private modality-specific subspaces to enable unified semantic segmentation with improved accuracy and cross-domain generalization on SemanticKITTI and nuScenes.
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.
ELiC delivers state-of-the-art real-time LiDAR geometry compression by propagating features across bit depths, selecting from a bag of encoders, and preserving Morton order.
Observability-constrained test-time prompt tuning for LiDAR semantic segmentation reweights spatial supervision using per-location reliability estimates from beam terminations and neighborhood support, with prompt adapters and temporal prototype alignment.
citing papers explorer
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DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs
DENALI is the first large-scale real-world dataset of space-time histograms from low-cost LiDARs for training models to perceive hidden objects via multi-bounce light cues.
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GIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
GIBLy is an architecture-agnostic lightweight layer that adds learnable geometric priors aligned with simple shapes to 3D semantic segmentation pipelines, delivering consistent mIoU gains (up to +11.5% on TS40K) with only 58K extra parameters.
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UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition
UniD-Shift decomposes 2D and 3D features into shared semantic and private modality-specific subspaces to enable unified semantic segmentation with improved accuracy and cross-domain generalization on SemanticKITTI and nuScenes.
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Sparsity-Aware Voxel Attention and Foreground Modulation for 3D Semantic Scene Completion
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.
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ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders
ELiC delivers state-of-the-art real-time LiDAR geometry compression by propagating features across bit depths, selecting from a bag of encoders, and preserving Morton order.
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No Adaptation Without Observation: Observability-Constrained Test-Time Prompt Tuning for LiDAR Semantic Segmentation
Observability-constrained test-time prompt tuning for LiDAR semantic segmentation reweights spatial supervision using per-location reliability estimates from beam terminations and neighborhood support, with prompt adapters and temporal prototype alignment.