Vernata: Self-Supervised Learning of LiDAR Point Representations | Paper Citation Record

Observation aaab5cc7-03d3-4c2c-8f0d-c8938391d60c · outbound

This paper cites Autonomous spot: Long-range autonomous exploration of extreme environments with legged locomotion,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Autonomous spot: Long-range autonomous exploration of extreme environments with legged locomotion,

Reference 1

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Observation b8981352-43eb-4cb7-aab7-a58f164f31eb · outbound

This paper cites Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk.

Vernata: Self-Supervised Learning of LiDAR Point Representations Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk

Reference 2

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This paper cites Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning,

Reference 3

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This paper cites Visual Language Maps for Robot Navigation.

Vernata: Self-Supervised Learning of LiDAR Point Representations Visual Language Maps for Robot Navigation

Reference 4

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Observation e6626521-3cb7-45ca-91b7-956c8be15072 · outbound

This paper cites Aligning cyber space with physical world: A comprehensive survey on embodied ai,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Aligning cyber space with physical world: A comprehensive survey on embodied ai,

Reference 5

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Observation 8b0ee215-d9dc-48b5-88bc-7e24dbd69c24 · outbound

This paper cites A Survey on Vision-Language-Action Models for Embodied AI.

Vernata: Self-Supervised Learning of LiDAR Point Representations A Survey on Vision-Language-Action Models for Embodied AI

Reference 6

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This paper cites Spot-compose: A framework for open-vocabulary object retrieval and drawer manipulation in point clouds,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Spot-compose: A framework for open-vocabulary object retrieval and drawer manipulation in point clouds,

Reference 7

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Observation 724c8b72-d90e-46d2-b571-8104868bc003 · outbound

This paper cites Lost & found: Tracking changes from egocentric observations in 3d dynamic scene graphs,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Lost & found: Tracking changes from egocentric observations in 3d dynamic scene graphs,

Reference 8

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Observation 2823aabf-5ce2-4028-b529-da3efc35e62a · outbound

This paper cites Shan and C.

Vernata: Self-Supervised Learning of LiDAR Point Representations Shan and C

Reference 9

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Observation cbd38cdc-817b-4caf-8613-0595b8e5db5a · outbound

This paper cites Loam: Lidar odometry and mapping in real-time.

Vernata: Self-Supervised Learning of LiDAR Point Representations Loam: Lidar odometry and mapping in real-time

Reference 10

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Observation 560f8de8-8c62-4bad-a0bd-4edc2d5ce178 · outbound

This paper cites Vilens: Visual, inertial, lidar, and leg odometry for all-terrain legged robots,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Vilens: Visual, inertial, lidar, and leg odometry for all-terrain legged robots,

Reference 11

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Observation e4999c8b-c544-4814-b422-59ec8642116f · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Scalability in perception for autonomous driving: Waymo open dataset,

Reference 12

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Observation 69322b56-a78e-43ff-8fda-3c4ff684bcf8 · outbound

This paper cites GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation.

Vernata: Self-Supervised Learning of LiDAR Point Representations GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

Reference 13

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Observation dc034cf1-2486-4fc4-9881-fcf35cb67057 · outbound

This paper cites Pointpainting: Sequential fusion for 3d object detection,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Pointpainting: Sequential fusion for 3d object detection,

Reference 14

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This paper cites Perception- aware multi-sensor fusion for 3d lidar semantic segmentation,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Perception- aware multi-sensor fusion for 3d lidar semantic segmentation,

Reference 15

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Observation 03179161-daf1-4cef-8e30-9aaa9820326e · outbound

This paper cites Scaling Laws for Neural Language Models.

Vernata: Self-Supervised Learning of LiDAR Point Representations Scaling Laws for Neural Language Models

Reference 16

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Observation ad030510-6921-4e20-a5f4-15230c5f7ac8 · outbound

This paper cites Explaining neural scaling laws,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Explaining neural scaling laws,

Reference 17

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Observation b91f01b6-fa37-4c65-acac-7dd20b7a3f3a · outbound

This paper cites Training Compute-Optimal Large Language Models.

Vernata: Self-Supervised Learning of LiDAR Point Representations Training Compute-Optimal Large Language Models

Reference 18

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Observation ed98c125-72a4-4ea9-94f4-ed35b411b92b · outbound

This paper cites Scaling Laws of Motion Forecasting and Planning -- Technical Report.

Vernata: Self-Supervised Learning of LiDAR Point Representations Scaling Laws of Motion Forecasting and Planning -- Technical Report

Reference 19

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Observation e2c1f123-f266-4742-92bf-09890aa5b518 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Vernata: Self-Supervised Learning of LiDAR Point Representations DINOv2: Learning Robust Visual Features without Supervision

Reference 20

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Observation 30ba75db-b2c3-420c-a16e-629ba63477a0 · outbound

This paper cites DINOv3.

Vernata: Self-Supervised Learning of LiDAR Point Representations DINOv3

Reference 21

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Observation 937d8cb5-8b51-4023-a836-1c125d257f5c · outbound

This paper cites SAM 3: Segment Anything with Concepts.

Vernata: Self-Supervised Learning of LiDAR Point Representations SAM 3: Segment Anything with Concepts

Reference 22

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Observation 2508e777-c63c-48af-94a7-58a98ead8381 · outbound

This paper cites Semantickitti: A dataset for semantic scene under- standing of lidar sequences,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Semantickitti: A dataset for semantic scene under- standing of lidar sequences,

Reference 23

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Observation c115d81f-2580-4811-a20f-bfd1aa27c5bc · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Scannet: Richly-annotated 3d reconstructions of indoor scenes,

Reference 24

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Observation ccd280b7-4339-48d1-b614-30c1c13a3413 · outbound

This paper cites A simple frame- work for contrastive learning of visual representations,.

Vernata: Self-Supervised Learning of LiDAR Point Representations A simple frame- work for contrastive learning of visual representations,

Reference 25

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Observation 2e352588-88fd-40bf-aa03-1f47f73dbd59 · outbound

This paper cites Towards a general-purpose foundation model for computational pathology,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Towards a general-purpose foundation model for computational pathology,

Reference 26

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Observation c53ceac2-694a-4865-9fbc-e98bc0f745d9 · outbound

This paper cites A foundation model for clinical-grade computational pathology and rare cancers detection,.

Vernata: Self-Supervised Learning of LiDAR Point Representations A foundation model for clinical-grade computational pathology and rare cancers detection,

Reference 27

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This paper cites Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,

Reference 28

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Observation 2887482f-1ffb-4331-ba0a-a5c573fdd384 · outbound

This paper cites Exploring data-efficient 3d scene understanding with contrastive scene contexts,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Exploring data-efficient 3d scene understanding with contrastive scene contexts,

Reference 29

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Observation 24af2482-da3a-4d77-8dc6-e9c1b228c589 · outbound

This paper cites Masked scene contrast: A scalable framework for unsupervised 3d representation learning,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Masked scene contrast: A scalable framework for unsupervised 3d representation learning,

Reference 30

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Observation f6fb403c-a624-4dcb-b8f2-024d4d673620 · outbound

This paper cites Sonata: Self-supervised learning of reliable point representations,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Sonata: Self-supervised learning of reliable point representations,

Reference 31

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This paper cites TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation.

Vernata: Self-Supervised Learning of LiDAR Point Representations TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation

Reference 32

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This paper cites Point transformer v3: Simpler faster stronger,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Point transformer v3: Simpler faster stronger,

Reference 33

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This paper cites Loftup: Learning a coordinate-based feature upsampler for vision foundation models,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Loftup: Learning a coordinate-based feature upsampler for vision foundation models,

Reference 34

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This paper cites A survey on self-supervised learning: Algorithms, applications, and future trends,.

Vernata: Self-Supervised Learning of LiDAR Point Representations A survey on self-supervised learning: Algorithms, applications, and future trends,

Reference 35

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This paper cites Momentum contrast for unsupervised visual representation learning,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Momentum contrast for unsupervised visual representation learning,

Reference 36

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This paper cites Unsupervised learning of visual features by contrasting cluster assign- ments,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Unsupervised learning of visual features by contrasting cluster assign- ments,

Reference 37

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Observation 7ea5a8ea-1266-4a74-bf96-b3f22b8f7235 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Masked autoencoders are scalable vision learners,

Reference 38

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This paper cites Emerging properties in self-supervised vision trans- formers,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Emerging properties in self-supervised vision trans- formers,

Reference 39

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Observation 9883d685-744e-4d32-b008-6280d92f50af · outbound

This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

Vernata: Self-Supervised Learning of LiDAR Point Representations iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 40

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This paper cites Pointcon- trast: Unsupervised pre-training for 3d point cloud understanding,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Pointcon- trast: Unsupervised pre-training for 3d point cloud understanding,

Reference 41

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This paper cites Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,

Reference 42

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This paper cites 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation,.

Vernata: Self-Supervised Learning of LiDAR Point Representations 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation,

Reference 43

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Observation cafb916d-ac32-41ff-add4-d21e0522082b · outbound

This paper cites Bidirectional projection network for cross dimension scene understanding,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Bidirectional projection network for cross dimension scene understanding,

Reference 44

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Observation 31655a4f-1da6-4bcb-9976-d37f9a6b3066 · outbound

This paper cites Lidar-camera panoptic segmentation via geometry-consistent and semantic-aware alignment,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Lidar-camera panoptic segmentation via geometry-consistent and semantic-aware alignment,

Reference 45

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Observation e246d262-3240-41aa-915b-78121bbcc03b · outbound

This paper cites Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining.

Vernata: Self-Supervised Learning of LiDAR Point Representations Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 46

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Observation 55e6d3a7-4ca1-48f2-9d8d-0f676bf5d9ac · outbound

This paper cites Image-to-lidar self-supervised distillation for autonomous driving data,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Image-to-lidar self-supervised distillation for autonomous driving data,

Reference 47

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Observation b271043f-477d-402f-b82e-fcdf52cc01d0 · outbound

This paper cites Three pillars improving vision foundation model distillation for lidar,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Three pillars improving vision foundation model distillation for lidar,

Reference 48

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Observation a5a1f7fe-3ab2-4a25-ac83-d486582aca8b · outbound

This paper cites Dino in the room: Leveraging 2d foundation models for 3d segmentation,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Dino in the room: Leveraging 2d foundation models for 3d segmentation,

Reference 49

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This paper cites Concerto: Joint 2d-3d self-supervised learning emerges spatial rep- resentations,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Concerto: Joint 2d-3d self-supervised learning emerges spatial rep- resentations,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:26:54.836133Z

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Observation a9e8b45c-e2fc-4feb-adc4-bda2fc4a40cf · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport,.

Vernata: Self-Supervised Learning of LiDAR Point Representations Sinkhorn distances: Lightspeed computation of optimal transport,

Reference 51

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Observation f53aa92f-d002-44f0-8897-6f104a7384f2 · outbound

This paper cites System design of the ultra mobility vehicle: A driving, bal- ancing, and jumping bicycle robot,.

Vernata: Self-Supervised Learning of LiDAR Point Representations System design of the ultra mobility vehicle: A driving, bal- ancing, and jumping bicycle robot,

Reference 52

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Observation 207b1a80-096f-4ddb-ad35-fd1218455edf · outbound

This paper cites The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over- union measure in neural networks,.

Vernata: Self-Supervised Learning of LiDAR Point Representations The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over- union measure in neural networks,

Reference 53

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