Paper Citation Record · LEDGER

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation

As of 11 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.20162.

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

pith.paper-citation-record.v1
2412.20162 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:34:15.094294Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b72cacbb-da17-4500-b114-05f4a8635f22 · outbound

This paper cites Ro- bodepth: Robust out-of-distribution depth estimation under corruptions,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Ro- bodepth: Robust out-of-distribution depth estimation under corruptions,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.717330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5efef85-ea16-449c-9267-d317068f4ebb · outbound

This paper cites Adabins: Depth estimation using adaptive bins,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Adabins: Depth estimation using adaptive bins,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.472377Z

Source-reported events for the cited work

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Observation 8fb6628a-fb02-4813-8c43-1df22e4c8a4d · outbound

This paper cites Transformer-based attention networks for continuous pixel-wise prediction,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Transformer-based attention networks for continuous pixel-wise prediction,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.445063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9722d67f-2932-4921-9802-ef447c0bae3b · outbound

This paper cites EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.756233Z

Source-reported events for the cited work

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Observation d018e618-4610-48ce-bf85-3f4ac1534f15 · outbound

This paper cites Repurposing diffusion-based image generators for monoc- ular depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Repurposing diffusion-based image generators for monoc- ular depth estimation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.769891Z

Source-reported events for the cited work

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Observation 9e873ee9-8f48-499f-9ec5-b5c730d07a11 · outbound

This paper cites Planning-oriented autonomous driving,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Planning-oriented autonomous driving,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.394194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c8100ab4-97da-46ca-8a73-1513d3e98d44 · outbound

This paper cites Robust monocular depth estimation under challenging conditions,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Robust monocular depth estimation under challenging conditions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.794420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1dca447-93da-456a-8a38-015492580445 · outbound

This paper cites Diffusion models for monoc- ular depth estimation: Overcoming challenging conditions,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Diffusion models for monoc- ular depth estimation: Overcoming challenging conditions,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.359819Z

Source-reported events for the cited work

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Observation e0997fd2-4546-4ed9-9a31-b7d8ac0a2184 · outbound

This paper cites R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.807073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae17f7ba-aafd-4253-bc7c-8558a7f32297 · outbound

This paper cites Defeat-net: General monocular depth via simultaneous unsupervised representation learning,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Defeat-net: General monocular depth via simultaneous unsupervised representation learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.300492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 324f4471-c181-45ba-b19d-009260e02808 · outbound

This paper cites Clip the gap: A single domain generalization approach for object detection,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Clip the gap: A single domain generalization approach for object detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.230968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a37e5c07-83a7-4f35-acc5-115dbc44e205 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.842943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d9006d1-d8e3-422b-a51b-0956eaafe142 · outbound

This paper cites Sqldepth: Generalizable self-supervised fine-structured monocular depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Sqldepth: Generalizable self-supervised fine-structured monocular depth estimation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.204750Z

Source-reported events for the cited work

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Observation df3d9cd2-302d-4590-b056-18874dce7048 · outbound

This paper cites Towards zero-shot scale-aware monocular depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Towards zero-shot scale-aware monocular depth estimation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.183638Z

Source-reported events for the cited work

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Observation f081494a-10d9-4986-972f-5ffa2fb9d3ce · outbound

This paper cites ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.870493Z

Source-reported events for the cited work

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Observation d5be21fb-8058-4ed5-b56e-297e0d3a98f4 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Depth anything: Unleashing the power of large-scale unlabeled data,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.877506Z

Source-reported events for the cited work

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Observation a48d22c4-7235-4c20-b7f8-924a22e0f245 · outbound

This paper cites Forkgan: Seeing into the rainy night,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Forkgan: Seeing into the rainy night,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.140046Z

Source-reported events for the cited work

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Observation 43d43d31-d1dd-4e75-9372-b6d8b33ac107 · outbound

This paper cites Single domain generalization for lidar semantic segmentation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Single domain generalization for lidar semantic segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.104112Z

Source-reported events for the cited work

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Observation 5cdcebf6-be5e-460b-9f31-2b1175cae0ba · outbound

This paper cites Turning a clip model into a scene text spotter,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Turning a clip model into a scene text spotter,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.056474Z

Source-reported events for the cited work

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Observation db8946ac-7ab9-4003-97d1-f23cdb0f27f0 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Learning transferable visual models from natural language supervision,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.017466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0b788cfc-8d5b-460c-b13c-701b08f9250c · outbound

This paper cites T2vlad: global-local sequence alignment for text-video retrieval,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation T2vlad: global-local sequence alignment for text-video retrieval,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.979410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 15b3f287-bbf3-49f4-9891-a64d46e10db3 · outbound

This paper cites What you see is what you read? improv- ing text-image alignment evaluation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation What you see is what you read? improv- ing text-image alignment evaluation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.937099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3524d3e-ebe8-438a-a284-0b9c36bec255 · outbound

This paper cites Clip-vip: Adapting pre-trained image-text model to video-language alignment,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Clip-vip: Adapting pre-trained image-text model to video-language alignment,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.897068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 64020a63-df0d-4fc6-9c08-b7b37adb167b · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Unleashing text-to-image diffusion models for visual perception,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.934753Z

Source-reported events for the cited work

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Observation 7ecda788-eab2-4c6a-bf70-7417b7af1682 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.941321Z

Source-reported events for the cited work

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Observation 278f7b62-2565-4c44-8307-12c572ff79f7 · outbound

This paper cites Self-supervised monocular depth estimation for all day images using domain separation.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Self-supervised monocular depth estimation for all day images using domain separation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.847274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c394d7b7-0329-4aff-8301-f71a9d684e5b · outbound

This paper cites Learning depth estimation for transparent and mirror surfaces,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Learning depth estimation for transparent and mirror surfaces,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.811921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af8e51d2-4b39-40f6-8b62-0c988e4b2b32 · outbound

This paper cites Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.761405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 98fcb1fe-6385-47f3-93d6-ee91e53c2b2c · outbound

This paper cites Self-supervised monocular depth estimation: Let’s talk about the weather,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Self-supervised monocular depth estimation: Let’s talk about the weather,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:16.269163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a0f566d6-77f2-46e7-828c-29f92c109052 · outbound

This paper cites Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Towards robust monocular depth estimation: Mixing datasets for zero-shot cross- dataset transfer,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.990303Z

Source-reported events for the cited work

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Observation a24ef0bf-5888-4fdf-90ba-5d3d295d63f7 · outbound

This paper cites Unidepth: Universal monocular metric depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Unidepth: Universal monocular metric depth estimation,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:14.996140Z

Source-reported events for the cited work

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Observation 8ba62ef2-b6a1-4ab2-98ca-08c3c83caace · outbound

This paper cites Metric3d: Towards zero-shot metric 3d prediction from a sin- gle image,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Metric3d: Towards zero-shot metric 3d prediction from a sin- gle image,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.659932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a0e53f0b-7626-4e6f-a832-f123c455a73f · outbound

This paper cites Can language understand depth?.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Can language understand depth?

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:15.013177Z

Source-reported events for the cited work

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Observation 98220c84-56a9-4c79-880a-bade461715be · outbound

This paper cites Clip-decoder: Zeroshot multilabel classification using multimodal clip aligned representations,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Clip-decoder: Zeroshot multilabel classification using multimodal clip aligned representations,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.605434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 458eb8e1-13fb-4e89-9fdf-5ce697d59230 · outbound

This paper cites Cora: Adapting clip for open- vocabulary detection with region prompting and anchor pre-matching,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Cora: Adapting clip for open- vocabulary detection with region prompting and anchor pre-matching,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.566850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c5e75a94-0844-469b-9374-ebc6e2860aee · outbound

This paper cites Text-to-concept (and back) via cross-model alignment,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Text-to-concept (and back) via cross-model alignment,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.531998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d3f848b2-b1ed-4103-a678-f522b7704357 · outbound

This paper cites Extract free dense labels from clip,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Extract free dense labels from clip,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:15.041413Z

Source-reported events for the cited work

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Observation e8b8ed8d-6fc9-4838-ad76-f97b48fa14bc · outbound

This paper cites ControlVideo: Training-free Controllable Text-to-Video Generation.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation ControlVideo: Training-free Controllable Text-to-Video Generation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:15.052306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ecbe7dc-9a91-4554-9441-4c93d07229ce · outbound

This paper cites Digging into self-supervised monocular depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Digging into self-supervised monocular depth estimation,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:15.058684Z

Source-reported events for the cited work

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Observation 7f48dd3e-81a4-4d74-bcd7-88d740930d48 · outbound

This paper cites 3d pack- ing for self-supervised monocular depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation 3d pack- ing for self-supervised monocular depth estimation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.470799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a029d4f6-bcec-4a81-9713-05372d098105 · outbound

This paper cites Self-supervised monocular depth estimation in the dark: Towards data distribution compensation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation Self-supervised monocular depth estimation in the dark: Towards data distribution compensation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.438913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b440253e-dd11-411b-8133-3efb0b8c9974 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation nuscenes: A multi- modal dataset for autonomous driving,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.417846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b0ac228c-fb13-4be7-86b8-82aab7021e3f · outbound

This paper cites 1 year, 1000 km: The oxford robotcar dataset,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation 1 year, 1000 km: The oxford robotcar dataset,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T23:34:15.086071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fa54803-7bf9-4647-9729-0ce88204b5f2 · outbound

This paper cites When the sun goes down: Repairing photometric losses for all-day depth estimation,.

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation When the sun goes down: Repairing photometric losses for all-day depth estimation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:34:15.367435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

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