---
title: Layered-Depth Prompting in MLLMs
url: https://www.emergentmind.com/topics/layered-depth-based-prompting-ldp
type: topic
---

# Layered-Depth Prompting in MLLMs

Searching arXiv for papers on Layered-Depth-Based Prompting and closely related depth-prompting work.
Layered-Depth-Based Prompting (LDP) is a prompting strategy that injects explicit depth-aware spatial structure into model inputs by organizing scene information according to depth strata or decoder-layer hierarchy. In the most direct usage of the term, LDP denotes the prompt augmentation method introduced in ByDeWay for multimodal large language models (MLLMs): a single RGB image is processed by monocular depth estimation, partitioned into closest, mid-range, and farthest regions, captioned per region, and then augmented with those depth-ordered captions before querying a downstream MLLM [2507.08679]. More broadly, the same phrase can describe a family of prompt-like mechanisms in which depth information is supplied as a structured conditioning signal rather than left implicit in raw pixels alone. This includes metric-depth prompting for depth foundation models, depth-prompt modules for sensor-agnostic depth estimation, and earlier layered-depth scene representations that expose occluded or side content through depth-ordered organization [2412.14015] [2405.11867] [1310.6377].

## 1. Definition and conceptual scope

In ByDeWay, LDP is defined as a **training-free** prompt augmentation framework for MLLMs that uses **monocular depth estimation** to divide an image into three depth strata—**closest layer**, **mid-range layer**, and **farthest layer**—and then appends region-specific captions for those strata to the original image-question prompt [2507.08679]. The stated purpose is to improve **spatial reasoning and grounding** without modifying model parameters, thereby making responses more grounded and less hallucinated [2507.08679].

The central intuition is that many MLLM errors arise from weak **scene grounding** rather than weak language modeling. A model may know what objects are plausible while still confusing **front/back**, **near/far**, foreground versus background, or the presence of an object in the queried region. LDP addresses this by converting a single RGB image into a structured textual summary of depth layers and presenting that summary as explicit prompt context [2507.08679].

Within the supplied literature, the term also admits a broader technical interpretation. Prompt Depth Anything does not formalize the exact name “Layered-Depth-Based Prompting,” but its core mechanism is described as a **multi-scale prompt fusion design** in which low-cost LiDAR depth is injected into a Depth Anything / DPT decoder at multiple scales to guide **metric depth** prediction [2412.14015]. Likewise, Any2Full reformulates depth completion as a **scale-prompting adaptation** of a pretrained monocular depth estimation model, using hierarchical scale prompts and multi-level fusion into the decoder [2603.05711]. These works suggest that “layered-depth-based prompting” can denote two related but distinct ideas: depth-layer prompting in the input/prompt space for reasoning models, and layered or hierarchical depth conditioning inside depth-estimation architectures.

A plausible implication is that LDP is less a single algorithm than a design principle: depth structure is externalized into a prompt or prompt-like conditioning pathway so that a pretrained model receives an explicit geometric scaffold instead of having to infer all spatial organization from RGB alone.

## 2. ByDeWay and the canonical LDP formulation

ByDeWay provides the clearest formalization of LDP as a prompt construction method for MLLMs [2507.08679]. The workflow begins with an RGB image and applies **Depth Anything V2** to produce a dense depth map \(D(x,y)\). The resulting representation is described as **affine-invariant inverse depth**, used for coarse scene segmentation rather than metric recovery [2507.08679].

The depth map is partitioned into three coarse regions using percentile thresholds computed at the **30th and 70th percentiles**. This yields masks for the **closest**, **mid-range**, and **farthest** regions, corresponding to top 30%, middle 40%, and bottom 30% of depth values [2507.08679]. Each masked region is then captioned independently with **KOSMOS-2**, which serves as a grounded vision-language model. The method therefore produces three depth-ordered captions, one per stratum, instead of a single holistic image caption [2507.08679].

These captions are concatenated with the original task prompt. The paper’s example prompt for POPE includes an instruction block, the question, a section titled “Image Caption about depth” with entries for **Closest**, **Mid-range**, and **Farthest**, and output constraints such as “Do not guess or assume” and “Return only a single 'yes' or 'no' string” [2507.08679]. The downstream MLLM is then queried with the original image plus this depth-aware textual augmentation, with **no fine-tuning**, **no parameter updates**, and **no architectural modification** [2507.08679].

The method is explicitly positioned against two alternatives. First, it differs from ordinary image captioning because its captions are **layer-specific**, **structured**, and **grounding-oriented** rather than holistic [2507.08679]. Second, it differs from chain-of-thought prompting because it supplies **external visual evidence** in textual form before reasoning rather than asking the model to generate internal reasoning traces [2507.08679]. In this respect, LDP functions as a scene decomposition prior expressed in natural language.

## 3. Algorithmic structure and prompt construction

The algorithmic structure of ByDeWay’s LDP can be summarized directly from the paper’s explicit workflow [2507.08679]. The image \(I\) is mapped to a depth field \(D(x,y)\), percentile thresholds \(T_1\) and \(T_2\) are computed at 30% and 70%, binary masks isolate three depth strata, each masked region is captioned by KOSMOS-2, and the resulting captions are appended to the task prompt in a structured template [2507.08679].

Several design choices are emphasized. The use of **three layers only** is deliberate: foreground, mid-ground, and background are considered sufficient to supply a concise and interpretable prompt [2507.08679]. The thresholds are **percentile-based** rather than absolute, which makes the partition depend on relative rank and therefore more robust across different images [2507.08679]. The captioner is grounded, rather than purely generative, because the goal is to constrain the MLLM toward evidence-supported entities and relations [2507.08679].

The prompt template itself is a substantive component of the method. The structure comprises a task instruction, the original question, a depth-caption block labeled by layer, and an output constraint [2507.08679]. This organization matters particularly on POPE, where answers are restricted to “yes” or “no,” and where hallucination suppression depends not only on extra scene context but also on discouraging unsupported guessing [2507.08679].

A common misconception is that LDP is merely “adding depth captions.” The paper’s formulation is narrower and more technical. The captions are not arbitrary summaries; they are generated from explicitly masked depth regions, ordered by depth, and injected into a prompt format designed to make spatial reference resolution easier for the MLLM [2507.08679]. The improvement is attributed to this structured evidence summary, not simply to longer prompts.

## 4. Empirical findings, utility, and limitations

ByDeWay evaluates LDP on **POPE** and **GQA**, using **subsets of 150 samples** from each dataset, and tests **gpt-4o**, **Qwen2.5-VL**, **ViLT**, and **BLIP** [2507.08679]. On POPE, the reported accuracies improve for all tested models: **gpt-4o** from **0.860** to **0.873**, **Qwen2.5-VL** from **0.7267** to **0.9000**, **ViLT** from **0.8533** to **0.9267**, and **BLIP** from **0.8733** to **0.9533** [2507.08679]. The paper also states that **precision, recall, and F1** improve, with particular emphasis on recall and F1 as indicators of fewer missed grounded positives and stronger overall classification quality [2507.08679].

On GQA, accuracy likewise improves: **Qwen2.5-VL** from **0.5007** to **0.6592**, **ViLT** from **0.527** to **0.627**, and **BLIP** from **0.5552** to **0.6704** [2507.08679]. The paper interprets these gains as evidence that depth-aware textual context helps with **object reasoning** and **spatial understanding**, especially on hallucination-sensitive tasks and for lighter-weight models such as BLIP and ViLT [2507.08679].

The practical advantages emphasized are that the method is **training-free**, **modular**, **model-agnostic**, and **black-box compatible** [2507.08679]. Because the downstream MLLM need only accept image-plus-text input, LDP can be used even when the model is closed-source or too large to retrain [2507.08679].

The most explicit limitation stated is **longer prompts**, which may increase inference latency or token cost [2507.08679]. The details also identify several implicit dependencies: monocular depth estimation must be good enough to supply a usable coarse partition, KOSMOS-2 captions must accurately describe masked regions, and three depth layers must be sufficient to summarize the scene [2507.08679]. This suggests that LDP inherits failure modes from both the depth estimator and the regional captioner; errors in either stage can propagate into the final prompt.

## 5. Relation to earlier layered-depth representations

Although ByDeWay applies LDP in prompt engineering for MLLMs, the broader idea of organizing scene information by depth layers has antecedents in layered scene representations for view synthesis and navigation. “Multiview Navigation based on Extended Layered Depth Image Representation” represents a navigation segment with an **extended LDI** that stores scene content in multiple depth-ordered layers and enlarges image support to preserve side information that would fall outside a reference camera’s field of view [1310.6377]. Its goal is to avoid repeating the same 3D point across multiple views, support synthesis of arbitrary views inside the segment, and transmit a self-contained representation for interactive navigation [1310.6377].

In that representation, **Layer 1** contains the visible surface from the reference camera, deeper layers contain progressively more occluded surfaces, and the enlarged support retains side regions from neighboring views that classical LDI would discard [1310.6377]. The decoder can reconstruct views by “unwarping” the extended LDI, and the representation removes **inter-view redundancy** at the representation stage rather than relying solely on multiview predictive coding [1310.6377]. The paper reports a **significant rate-distortion gain** compared with classical multiview compression approaches in the navigation scenario [1310.6377].

A related but different use of layered depth appears in “3D Photography using Context-aware Layered Depth Inpainting,” which converts a single RGB-D image into a **3D photo** using an **LDI with explicit pixel connectivity** and a context-aware inpainting pipeline for occluded regions [2004.04727]. The method identifies depth discontinuities, disconnects foreground and background across the edge, defines local synthesis and context regions, predicts missing depth edges, then inpaints color and depth [2004.04727]. The paper is not a prompting method in the language-model sense, but it treats layered geometry and connectivity as a structured scaffold for conditional generation [2004.04727].

These earlier works do not define LDP as a prompt augmentation technique for MLLMs. However, they demonstrate a recurring technical principle: depth-ordered decomposition can preserve hidden structure, resolve occlusions, and provide a compact or conditionally useful representation of a scene [1310.6377] [2004.04727]. This suggests that ByDeWay’s LDP is part of a longer line of work in which depth layers are used to expose scene organization explicitly rather than leaving it latent.

## 6. Adjacent prompt-based depth paradigms

Depth prompting also appears in depth-estimation research, though often without the exact ByDeWay meaning of LDP. “Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation” reframes metric depth estimation as a prompting problem in which **low-cost LiDAR depth** acts as a **metric prompt** for a **Depth Anything v2** backbone with a **ViT + DPT decoder** [2412.14015]. The prompt is injected at **multiple decoder scales** by resizing LiDAR depth to stage resolution, encoding it with a shallow convolutional network, projecting it with a **zero-initialized \(1\times1\) convolution**, and fusing it additively with decoder features [2412.14015]. The paper describes this as a **multi-scale prompt fusion design** and reports state-of-the-art results on **ARKitScenes** and **ScanNet++**, including **L1 0.0135** and **RMSE 0.0326** on ARKitScenes at \(384 \times 512\), with testing up to **\(2160 \times 3840\)** [2412.14015].

“Depth Prompting for Sensor-Agnostic Depth Estimation” likewise treats sparse depth as a prompt, but not as layered depth strata [2405.11867]. The method uses a **depth prompt encoder** \(f_{\mathcal{E}}\), a frozen monocular depth foundation model \(f_{\mathcal{F}}\), and an **adaptive affinity decoder** \(f_{\mathcal{D}}\) to produce an adaptive affinity map for spatial propagation [2405.11867]. A least-squares alignment
\[
\hat{p} = \min_{p} \left\| p\hat{D}_{I}^{V} - D_S \right\|_F
\]
converts relative monocular depth to **metric absolute scale** [2405.11867]. The paper explicitly states that this is **prompt-based**, but **not layered-depth-based prompting** in the sense of discrete depth layers or layer-wise prompt tokens [2405.11867].

Any2Full extends this architectural direction by treating sparse metric depth as **scale prompts** for a frozen pretrained monocular depth estimator, specifically **Depth Anything v2**, using a **Scale-Aware Prompt Encoder** with **Local Enrichment**, **Global Propagation**, and hierarchical FiLM-style fusion into decoder features [2603.05711]. The method is described as **one-stage**, **domain-general**, and **pattern-agnostic**, and the paper reports that it **outperforms OMNI-DC by 32.2\% in average AbsREL** and provides a **1.4\times speedup over PriorDA** with the same MDE backbone [2603.05711].

Taken together, these papers show that “prompting with depth” currently spans at least three regimes. First, ByDeWay uses textualized depth layers to improve reasoning in MLLMs [2507.08679]. Second, Prompt Depth Anything and Any2Full use hierarchical or multi-scale depth prompts inside depth foundation models [2412.14015] [2603.05711]. Third, sensor-agnostic depth prompting uses sparse depth as a continuous prompt embedding rather than as discrete layers [2405.11867]. A plausible implication is that the common denominator is not the modality alone, but the conversion of depth from a latent cue into an explicit conditioning interface.

## 7. Interpretation, significance, and boundaries of the term

Within the available literature, the most precise use of “Layered-Depth-Based Prompting” is the ByDeWay method for MLLMs [2507.08679]. In that strict sense, LDP means depth-stratified region captioning appended to the image-question prompt, with the aim of improving **hallucination resistance** and **visual reasoning** in a **zero-training setting** [2507.08679]. Its significance lies in showing that prompt-level spatial grounding can improve multimodal reasoning without any model updates.

At the same time, the term is used more loosely in some technical syntheses to group together prompting methods that inject depth information in layered, multi-scale, or hierarchically structured form [2412.14015] [2603.05711]. This broader usage should be treated carefully. Depth Prompting for Sensor-Agnostic Depth Estimation explicitly distinguishes itself from layered-depth prompting, since it does **not** discretize depth into layers, use ordered depth strata, or build prompts as a stack of layers [2405.11867]. Conversely, earlier LDI and layered inpainting methods are highly relevant conceptually, but they are not prompt engineering methods in the MLLM sense [1310.6377] [2004.04727].

A common source of confusion is the abbreviation **LDP** itself. In a different research area, “LDP” denotes **local differential privacy**, as in “Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space” [2201.07469]. That literature concerns privacy-preserving perturbation and aggregation and is unrelated to layered-depth prompting despite the shared acronym [2201.07469].

The current evidence suggests that layered depth is useful when the target task depends on scene grounding, occlusion order, scale anchoring, or geometric consistency. In MLLMs, this manifests as improved handling of front/back and near/far relations [2507.08679]. In depth estimation, it manifests as better metric consistency and cross-pattern robustness through prompt fusion into pretrained backbones [2412.14015] [2603.05711]. A plausible implication is that future work will continue to move depth from an implicit latent variable to an explicit prompt substrate, whether expressed as text, masks, scale cues, or hierarchical decoder modulation.

Source: https://www.emergentmind.com/topics/layered-depth-based-prompting-ldp