Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation | Paper Citation Record

Observation d0e2216c-4d7f-4003-ab7e-83861d5609e1 · outbound

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

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Kpconv: Flexible and deformable convolution for point clouds,

Reference 1

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This paper cites Point transformer V2: grouped vector attention and partition-based pooling,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Point transformer V2: grouped vector attention and partition-based pooling,

Reference 2

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This paper cites Point transformer V3: simpler, faster, stronger,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Point transformer V3: simpler, faster, stronger,

Reference 3

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This paper cites Cylindrical and asymmetrical 3d convolution networks for lidar segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Cylindrical and asymmetrical 3d convolution networks for lidar segmentation,

Reference 4

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Observation a5e13e00-f449-4c71-a29e-ece1eadf2efc · outbound

This paper cites Point-to-voxel knowledge distillation for lidar semantic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Point-to-voxel knowledge distillation for lidar semantic segmentation,

Reference 5

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Observation 1260078f-f581-4b1f-95f1-8773c42d044c · outbound

This paper cites Polarnet: An improved grid representation for online lidar point clouds semantic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Polarnet: An improved grid representation for online lidar point clouds semantic segmentation,

Reference 6

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Observation d2d70a49-ec96-4b5e-b14e-92b688671bcb · outbound

This paper cites Effi- cientlps: Efficient lidar panoptic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Effi- cientlps: Efficient lidar panoptic segmentation,

Reference 7

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Observation 2e3ca0b0-a98b-446a-8ac1-c3f17f578056 · outbound

This paper cites FLARES: fast and accurate lidar multi-range semantic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation FLARES: fast and accurate lidar multi-range semantic segmentation,

Reference 8

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Observation 4469254e-5160-402f-a23d-e57e200fdb22 · outbound

This paper cites Rangenet ++: Fast and accurate lidar semantic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Rangenet ++: Fast and accurate lidar semantic segmentation,

Reference 9

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Observation 6336c324-0a9d-451d-a838-5b95f5f0ee6c · outbound

This paper cites Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds,

Reference 10

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Observation 2360246b-4519-4035-b3aa-23718c613534 · outbound

This paper cites Real time semantic segmentation of high resolution automotive lidar scans,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Real time semantic segmentation of high resolution automotive lidar scans,

Reference 11

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Observation bc48b039-b346-49f4-980b-180634e8632c · outbound

This paper cites Uncertainty-aware lidar panoptic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Uncertainty-aware lidar panoptic segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.829205Z

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Observation 9b6a55fb-7599-4403-9825-6d21f989c3c2 · outbound

This paper cites Panoptic-polarnet: Proposal-free lidar point cloud panoptic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Panoptic-polarnet: Proposal-free lidar point cloud panoptic segmentation,

Reference 13

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Observation 70c1baf2-8f05-4c52-bfc5-f9265679c84f · outbound

This paper cites On calibration of modern neural networks,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation On calibration of modern neural networks,

Reference 14

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Observation 1cc4f485-1ed5-4807-b025-94c5500d6ae5 · outbound

This paper cites Evidential deep learning to quantify classification uncertainty,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Evidential deep learning to quantify classification uncertainty,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.798428Z

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Observation 0a2d541d-7af6-4f27-b511-543a3318e853 · outbound

This paper cites Open-set lidar panoptic segmentation guided by uncertainty-aware learning,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Open-set lidar panoptic segmentation guided by uncertainty-aware learning,

Reference 16

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Observation cfe332cf-9800-443d-8e82-843084913e0c · outbound

This paper cites Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding,

Reference 17

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Observation 86cd6c5f-d1e6-4c63-a9b0-7244a81661a0 · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision?.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation What uncertainties do we need in bayesian deep learning for computer vision?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.808027Z

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Observation 33402fb5-d579-4af8-b990-288b68e5e14d · outbound

This paper cites A probabilistic u-net for segmentation of ambiguous images,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation A probabilistic u-net for segmentation of ambiguous images,

Reference 19

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Observation d1202a13-fd84-4370-9f2c-174872594e78 · outbound

This paper cites Semantickitti: A dataset for semantic scene understanding of lidar sequences,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Semantickitti: A dataset for semantic scene understanding of lidar sequences,

Reference 20

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Observation 27a78924-6d95-4106-88d6-e5ad1edf68b0 · outbound

This paper cites Uncertainty-aware point cloud segmentation for infrastructure projects using bayesian deep learning,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Uncertainty-aware point cloud segmentation for infrastructure projects using bayesian deep learning,

Reference 21

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Observation 883fab35-1360-4c45-89a0-a1c5f9efc74c · outbound

This paper cites Learning Confidence for Out-of-Distribution Detection in Neural Networks.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Learning Confidence for Out-of-Distribution Detection in Neural Networks

Reference 22

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Observation 597a0f97-cc8e-459c-ba00-102ccde15eab · outbound

This paper cites Ad- dressing failure prediction by learning model confidence,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Ad- dressing failure prediction by learning model confidence,

Reference 23

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Observation cca0ec40-e43f-43cf-9fc2-e29641c02fed · outbound

This paper cites Confidence estimation via auxiliary models,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Confidence estimation via auxiliary models,

Reference 24

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Observation c77ab3e9-7512-4e66-97ff-c7f3fa67d7a0 · outbound

This paper cites Fsnet: A failure detection framework for semantic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Fsnet: A failure detection framework for semantic segmentation,

Reference 25

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Observation b75a7cb5-b53e-46b8-9c15-f383ef9cc94a · outbound

This paper cites Scaling out-of-distribution detection for real-world settings,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Scaling out-of-distribution detection for real-world settings,

Reference 26

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Observation 3339705e-101f-4220-89d1-71bc9a9f44dd · outbound

This paper cites Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation

Reference 27

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Observation 7a5ea4db-7766-4a02-a796-67db686796ff · outbound

This paper cites Uncertainty estimation and out-of-distribution detection for lidar scene semantic segmenta- tion,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Uncertainty estimation and out-of-distribution detection for lidar scene semantic segmenta- tion,

Reference 28

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Observation 314c8867-b08d-4d42-b83e-3639b6f8d4cf · outbound

This paper cites The MNIST database of handwritten digit images for machine learning research [best of the web],.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation The MNIST database of handwritten digit images for machine learning research [best of the web],

Reference 29

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This paper cites Learning multiple layers of features from tiny images,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Learning multiple layers of features from tiny images,

Reference 30

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Observation 695bab6c-92ff-4081-a386-e8bd61c606da · outbound

This paper cites Identifying out-of-domain objects with dirichlet deep neural networks,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Identifying out-of-domain objects with dirichlet deep neural networks,

Reference 31

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Observation ca4cc2f9-fd2b-401d-ac4b-47239faf9135 · outbound

This paper cites Deep evidential uncertainty esti- mation for semantic segmentation under out-of-distribution obstacles,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Deep evidential uncertainty esti- mation for semantic segmentation under out-of-distribution obstacles,

Reference 32

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Observation 8bd74889-ed74-4405-91b4-9621fef9cb53 · outbound

This paper cites Posterior network: Un- certainty estimation without OOD samples via density-based pseudo- counts,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Posterior network: Un- certainty estimation without OOD samples via density-based pseudo- counts,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.734249Z

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Observation 6137ae17-7815-4143-87df-057f7397dfb7 · outbound

This paper cites KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,

Reference 34

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Observation cc41a97a-82a3-418e-a1b9-ac6d64018dce · outbound

This paper cites Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking,

Reference 35

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Observation 5396bb71-192d-4832-8bfc-61c40bedae94 · outbound

This paper cites Revisiting essential and nonessential settings of evidential deep learning,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Revisiting essential and nonessential settings of evidential deep learning,

Reference 36

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Observation d47bafad-b660-47fc-9483-41049cd9ab14 · outbound

This paper cites Information aware max-norm dirichlet networks for predictive uncertainty estimation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Information aware max-norm dirichlet networks for predictive uncertainty estimation,

Reference 37

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Observation 31437a1a-b484-4c65-8935-5d5aec1f1546 · outbound

This paper cites Is epistemic uncertainty faithfully represented by evidential deep learning methods?.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Is epistemic uncertainty faithfully represented by evidential deep learning methods?

Reference 38

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Observation af5fb702-8356-4fac-b98e-c234bfa5bb33 · outbound

This paper cites Research.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Research

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.731901Z

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Observation 10525b3b-1a45-47af-ab66-28b171f1823d · outbound

This paper cites SemanticTHAB: A high resolution lidar dataset,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation SemanticTHAB: A high resolution lidar dataset,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.729380Z

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Observation 1bcc82d3-b8ea-4182-b41d-297aa6dd06c2 · outbound

This paper cites Panoptic-CUDAL Technical Report: Ru- ral Australia Point Cloud Dataset in Rainy Conditions.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Panoptic-CUDAL Technical Report: Ru- ral Australia Point Cloud Dataset in Rainy Conditions

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:04:56.656894Z

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Observation 4e8133c2-d945-4d44-b866-c1d278af2135 · outbound

This paper cites Sensor equivariance by lidar projection images,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Sensor equivariance by lidar projection images,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.750028Z

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Observation c57df17c-bb3f-4419-89f2-3b135d5537f6 · outbound

This paper cites Efficientnetv2: Smaller models and faster training,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Efficientnetv2: Smaller models and faster training,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.767291Z

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Observation 1524b441-522f-4aa8-aeba-c62f07bd7c01 · outbound

This paper cites Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.858042Z

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Observation 2a502f06-b257-4fa9-b97b-403062055c17 · outbound

This paper cites Uncertainty-aware panoptic segmentation,.

Invascal: Inverse-Vacuity Self-Calibration for Uncertainty-Aware LiDAR Range-View Semantic Segmentation Uncertainty-aware panoptic segmentation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T11:24:55.842248Z

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