Paper Citation Record · LEDGER

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2412.11241.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.11241 v1

Coverage vector

measured 38 of 38 reference resolution

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measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:30:48.262458Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T19:48:50.558758Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation d5276182-9127-4afc-9c93-38ed8ca78fed · outbound

This paper cites Rgb-(d) scene labeling: Features and algorithms,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Rgb-(d) scene labeling: Features and algorithms,

Reference 1

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Observation 99a79db2-7144-441e-9680-805e8c01e0cf · outbound

This paper cites Perceptual organization and recognition of indoor scenes from rgb-d images,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Perceptual organization and recognition of indoor scenes from rgb-d images,

Reference 2

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Observation 7d21eb9a-c365-46a4-a9eb-63dd09d05d31 · outbound

This paper cites Real-time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Real-time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision

Reference 3

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Observation 5031f383-25d1-4aec-84e6-abf85757ed85 · outbound

This paper cites Refining image segmentation by integration of edge and region data,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Refining image segmentation by integration of edge and region data,

Reference 4

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Observation 0f214b09-8394-41ac-959e-dfa033bebadf · outbound

This paper cites Interactive graph cut based segmentation with shape priors,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Interactive graph cut based segmentation with shape priors,

Reference 5

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Observation a163a643-d1a0-4ff6-bc45-483d3e2c22a6 · outbound

This paper cites Graph cut based image segmentation with connectivity priors,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Graph cut based image segmentation with connectivity priors,

Reference 6

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Observation 1eea81d1-d399-414a-9408-aaadf070cd86 · outbound

This paper cites A segmentation based robust deep learning framework for multimodal retinal image registration,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots A segmentation based robust deep learning framework for multimodal retinal image registration,

Reference 7

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Observation e31c18af-fd3c-421f-a6cc-2c2641252173 · outbound

This paper cites On advantages of mask-level recognition for outlier-aware segmentation,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots On advantages of mask-level recognition for outlier-aware segmentation,

Reference 8

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This paper cites Lstm-cf: Unifying context modeling and fusion with lstms for rgb-d scene la- beling,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Lstm-cf: Unifying context modeling and fusion with lstms for rgb-d scene la- beling,

Reference 9

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Observation 661305c9-faee-4eb4-871a-d2a608769857 · outbound

This paper cites Learning com- mon and specific features for rgb-d semantic segmentation with deconvolutional networks,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Learning com- mon and specific features for rgb-d semantic segmentation with deconvolutional networks,

Reference 10

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Observation 36b16190-1e0d-4282-9208-ff7d174c6d2b · outbound

This paper cites Rdfnet: Rgb-d multi-level residual feature fusion for indoor semantic segmentation,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Rdfnet: Rgb-d multi-level residual feature fusion for indoor semantic segmentation,

Reference 11

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This paper cites Fusenet: In- corporating depth into semantic segmentation via fusion-based cnn architecture,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Fusenet: In- corporating depth into semantic segmentation via fusion-based cnn architecture,

Reference 12

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This paper cites Depth-aware cnn for rgb-d segmenta- tion,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Depth-aware cnn for rgb-d segmenta- tion,

Reference 13

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Observation 6027e85f-d4c4-4b79-be31-27a3f54c95fa · outbound

This paper cites Two-stage cascaded decoder for semantic segmentation of rgb-d images,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Two-stage cascaded decoder for semantic segmentation of rgb-d images,

Reference 14

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Observation 0c459779-4990-4cee-98e0-c02f7f510d29 · outbound

This paper cites Indoor seg- mentation and support inference from rgbd images,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Indoor seg- mentation and support inference from rgbd images,

Reference 15

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Observation adca4e3f-d8f1-40f4-a645-138576b3b7ae · outbound

This paper cites Indoor Semantic Segmentation using depth information.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Indoor Semantic Segmentation using depth information

Reference 16

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Observation 7e7c1307-5dac-4396-b427-279311d37b0f · outbound

This paper cites Learning rich features from rgb-d images for object detection and segmenta- tion,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Learning rich features from rgb-d images for object detection and segmenta- tion,

Reference 17

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Observation def459bd-b1d6-4317-8e7c-6c1328a33693 · outbound

This paper cites Kinectfusion: Real-time dense surface mapping and tracking,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Kinectfusion: Real-time dense surface mapping and tracking,

Reference 18

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Observation 0e1eda03-df2a-4335-88b6-c24bfcc48d0e · outbound

This paper cites Chisel: Real time large scale 3d reconstruction onboard a mobile device using spatially hashed signed distance fields.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Chisel: Real time large scale 3d reconstruction onboard a mobile device using spatially hashed signed distance fields

Reference 19

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Observation 9d669377-67f3-421c-9487-376992a99016 · outbound

This paper cites V oxblox: Incremental 3d euclidean signed distance fields for on- board mav planning,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots V oxblox: Incremental 3d euclidean signed distance fields for on- board mav planning,

Reference 20

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Observation 42cc2908-4dea-4d16-aa32-f39ddd32b21f · outbound

This paper cites V olumetric instance-aware semantic mapping and 3d object discovery,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots V olumetric instance-aware semantic mapping and 3d object discovery,

Reference 21

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This paper cites V oxfield: Non-projective signed distance fields for online planning and 3d reconstruction,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots V oxfield: Non-projective signed distance fields for online planning and 3d reconstruction,

Reference 22

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Observation 84ff314a-a0ae-4d14-a582-06246d8bbc79 · outbound

This paper cites Panoptic multi-tsdfs: a flexible repre- sentation for online multi-resolution volumetric mapping and long- term dynamic scene consistency,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Panoptic multi-tsdfs: a flexible repre- sentation for online multi-resolution volumetric mapping and long- term dynamic scene consistency,

Reference 23

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This paper cites Se- manticfusion: Dense 3d semantic mapping with convolutional neural networks,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Se- manticfusion: Dense 3d semantic mapping with convolutional neural networks,

Reference 24

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This paper cites Co-fusion: Real-time segmentation, tracking and fusion of multiple objects,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Co-fusion: Real-time segmentation, tracking and fusion of multiple objects,

Reference 25

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Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Maskfusion: Real-time recog- nition, tracking and reconstruction of multiple moving objects,

Reference 26

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This paper cites Panoptic segmentation,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Panoptic segmentation,

Reference 27

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This paper cites Fast and ac- curate semantic mapping through geometric-based incremental seg- mentation,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Fast and ac- curate semantic mapping through geometric-based incremental seg- mentation,

Reference 28

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This paper cites Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,

Reference 29

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This paper cites Robust statistical estimation and segmentation of multiple subspaces,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Robust statistical estimation and segmentation of multiple subspaces,

Reference 30

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This paper cites A density-based spatial clustering of application with noise,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots A density-based spatial clustering of application with noise,

Reference 31

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This paper cites A volumetric method for building complex models from range images,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots A volumetric method for building complex models from range images,

Reference 32

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This paper cites Dart: Dense articulated real-time tracking.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Dart: Dense articulated real-time tracking

Reference 33

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Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Dynamicfusion: Recon- struction and tracking of non-rigid scenes in real-time,

Reference 34

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This paper cites Dynamic high resolution deformable articulated tracking,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Dynamic high resolution deformable articulated tracking,

Reference 35

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This paper cites Signed distance fields: A natural representation for both mapping and planning,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Signed distance fields: A natural representation for both mapping and planning,

Reference 36

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This paper cites YOLO by Ultralytics,.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots YOLO by Ultralytics,

Reference 37

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This paper cites Available: https://github.com/ultralytics/ultralytics.

Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots Available: https://github.com/ultralytics/ultralytics

Reference 2023

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Pith citing papers

Observation 7c3ab5d9-983f-4b34-a2c2-9c33de270220 · inbound

Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors cites this paper.

Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots

Reference 23

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Observation 54e4ae1b-76dc-4664-a87b-737d17d70ce3 · inbound

Bio-Inspired Hybrid Map: Spatial Implicit Local Frames and Topological Map for Mobile Cobot Navigation cites this paper.

Bio-Inspired Hybrid Map: Spatial Implicit Local Frames and Topological Map for Mobile Cobot Navigation Volumetric Mapping with Panoptic Refinement via Kernel Density Estimation for Mobile Robots

Reference 27

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