Title: ViSPLA: Visual Iterative Self-Prompting for Language-Guided 3D Affordance Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Robotics

Affordance Estimation

Vision-language Model
Abstract: We address the problem of language-guided 3D affordance prediction, a core capability for embodied agents interacting with unstructured environments. Existing methods often rely on fixed affordance categories or require external expert prompts, limiting their ability to generalize across different objects and interpret multi-step instructions. In this work, we introduce $\textit{ViSPLA}$, a novel iterative self-prompting framework that leverages the intrinsic geometry of predicted masks for continual refinement. We redefine affordance detection as a language-conditioned segmentation task: given a 3D point cloud and language instruction, our model predicts a sequence of refined affordance masks, each guided by differential geometric feedback including Laplacians, normal derivatives, and curvature fields. This feedback is encoded into visual prompts that drive a multi-stage refinement decoder, enabling the model to self-correct and adapt to complex spatial structures. To further enhance precision and coherence, we introduce Implicit Neural Affordance Fields, which define continuous probabilistic regions over the 3D surface without additional supervision. Additionally, our Spectral Convolutional Self-Prompting module operates in the frequency domain of the point cloud, enabling multi-scale refinement that captures both coarse and fine affordance structures. Extensive experiments demonstrate that $\textit{ViSPLA}$ achieves state-of-the-art results on both seen and unseen objects on two benchmark datasets. Our framework establishes a new paradigm for open-world 3D affordance reasoning by unifying language comprehension with low-level geometric perception through iterative refinement.

ViSPLA: Visual Iterative Self-Prompting for Language-Guided 3D Affordance Learning

Abstract

We address the problem of language-guided 3D affordance prediction, a core capability for embodied agents interacting with unstructured environments. Existing methods often rely on fixed affordance categories or require external expert prompts, limiting their ability to generalize across different objects and interpret multi-step instructions. In this work, we introduce ViSPLA , a novel iterative selfprompting framework that leverages the intrinsic geometry of predicted masks for continual refinement. We redefine affordance detection as a language-conditioned segmentation task: given a 3D point cloud and language instruction, our model predicts a sequence of refined affordance masks, each guided by differential geometric feedback including Laplacians, normal derivatives, and curvature fields. This feedback is encoded into visual prompts that drive a multi-stage refinement decoder, enabling the model to self-correct and adapt to complex spatial structures. To further enhance precision and coherence, we introduce Implicit Neural Affordance Fields, which define continuous probabilistic regions over the 3D surface without additional supervision. Additionally, our Spectral Convolutional Self-Prompting module operates in the frequency domain of the point cloud, enabling multi-scale refinement that captures both coarse and fine affordance structures. Extensive experiments demonstrate that ViSPLA achieves state-of-the-art results on both seen and unseen objects on two benchmark datasets. Our framework establishes a new paradigm for open-world 3D affordance reasoning by unifying language comprehension with low-level geometric perception through iterative refinement. Project Website

1 Introduction

Affordance, initially conceptualized by Gibson [1], defines the potential action possibilities that objects present to an agent. The evolution of robotic systems toward increasingly unstructured environments necessitates a fundamental paradigm shift in how we conceptualize affordance detection. Formally, we can represent the affordance detection problem as a mapping function f θ : ( P ) ↦→A , where P ∈ R N × 3 denotes a point cloud with N points, and A is the binary affordance mask indicating interactable regions. As shown in Figure 1(a), traditional approaches constrain this mapping to a limited set of predefined K affordance categories A = { a k } ; k = { 1 , 2 , .., K } , which fundamentally restricts the generalization capability and operational flexibility in dynamic, real-world environments [2]. Although conventional methodologies have predominantly focused on visual modalities, attempting to infer functionality from geometric structures or 2D visual features, such approaches inherently lack the semantic reasoning capabilities essential for complex interaction scenarios. The semantic gap between low-level perceptual features and high-level functional understanding represents a critical

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limitation that inhibits the deployment of autonomous agents in real-world contexts. Language-guided affordance prediction offers a mathematically elegant solution to this complex problem.

Language-guided affordance detection from 3D point clouds represents a pivotal direction in embodied AI, serving as the critical bridge between perception and manipulation in the physical world. By conditioning the affordance function on natural language instructions, we can formulate a more generalizable mapping: F θ : ( P , L ) ↦→M , where θ represents the learnable parameters, L represents a linguistic instruction, and M∈{ 0 , 1 } N is the binary affordance mask (visualized in Figure 1(b)). This formulation opens avenues for handling more diverse and complex scenarios-potentially allowing models to interpret novel affordance types via linguistic cues, handle multi-step tasks through decomposed predictions, and relate instructions to affordances at different levels of granularity. Recent progress in Large Language Models (LLMs) has shown impressive capabilities in sequential reasoning and knowledge grounding [7], but these models are often decoupled from 3D perception. Meanwhile, 3D affordance detection methods typically remain limited to static, single-affordance settings, with little capacity to handle instructions requiring compositional or context-aware reasoning across multiple object parts. This disconnect motivates a more integrated, multimodal approach that unifies linguistic understanding with spatial perception.

To this end, we propose an iterative self-prompting-based 3D affordance detection paradigm that bridges the gap between language understanding and affordance segmentation through geometric feedback-driven refinement, as shown in Figure 1(c). Unlike prior approaches that perform singlepass inference, our method implements a closed-loop system where each predicted affordance mask is used to generate geometric self-prompts that refine subsequent predictions. Mathematically, we formulate this as: M t = f θ ( P , G ( M t -1 ) , L ) ; t ∈ { 1 , 2 , .., T } , where M 0 = f θ ( P , L ) is the initial affordance mask predicted from a language-conditioned decoder, and G denotes the geometric prompt generator that extracts differential features (e.g., curvature, normal derivatives) from M t -1 . The final refined mask M T integrates both semantic guidance and geometric consistency, enabling robust and generalizable affordance segmentation across varying levels of granularity and complexity.

This approach addresses several critical challenges in the field: (1) Existing single-pass inference methods lack the ability to iteratively refine predictions, often leading to suboptimal segmentation, especially on complex geometries; (2) most affordance models fail to leverage intrinsic geometric structure for mask refinement, relying instead on language cues alone, which limits localization

accuracy, especially in complex or ambiguous settings; (3) the disconnect between high-level language semantics and low-level geometric features, hindering precise and context-aware affordance prediction across multiple scales; and (4) the difficulty of achieving fine-grained, geometrically consistent affordance boundaries without dense supervision, particularly in sparse or noisy point clouds. In summary, our contributions are:

We introduce Visual Iterative Self-Prompting for 3D Affordance Learning (ViSPLA), which leverages geometric features from predicted masks as visual prompts for progressive refinement. Unlike existing single-pass methods, our approach establishes a self-improving cycle that enhances precision across multiple object geometries.

We propose a novel Differential Geometric Self-Prompting mechanism that extracts mathematical properties (Laplacians, curvatures, normal derivatives) from predicted masks to guide subsequent iterations. This approach enables more accurate affordance localization by incorporating intrinsic geometric cues rather than relying solely on language.

We develop a Multi-Stage Refinement Decoder that creates dynamic mappings between language tokens and point features. By injecting LLM reasoning into dense point features, our approach bridges high-level semantic understanding with low-level geometric representation.

We introduce an Implicit Neural Affordance Field technique that learns a smooth, continuous function over the 3D object to refine affordance boundaries and enforce geometric consistency, even without extra supervision. In tandem, our Spectral Convolutional Self-Prompting module analyzes and enhances affordance predictions at multiple structural scales, enabling the model to capture both broad shapes and fine details for robust and accurate segmentation, especially in sparse or noisy scenarios.

We demonstrate that fine-tuning pre-trained MLLMs through our self-prompting framework yields superior performance on both seen and unseen scenarios.

2 Related Work

2.1 Affordance Detection

Affordance detection aims to identify functionally interactive regions on objects, crucial for enabling robotic agents to manipulate and reason about the physical world. Early works in 2D explored objectlevel affordances using CNNs [3], later extending to language-conditioned queries [8], but remained limited to coarse spatial reasoning. Subsequent efforts [9, 10] introduced fine-grained part-level detection but were restricted to fixed affordance taxonomies. The emergence of 3D datasets like 3D AffordanceNet [4] and PartNet [11] enabled point cloud-based affordance learning. IAGNet [12] utilized 2D human-object interactions to guide 3D segmentation, while OpenAD [13] advanced open-vocabulary affordance detection using joint text-geometry embeddings. However, these models still rely on static label spaces and do not support complex instruction understanding. Recent methods like LASO [14] incorporate language into 3D affordance prediction, but often assume one-to-one mappings between text and affordance, lacking support for multi-step or compositional reasoning. Chu et al. [15] use LLMs for cross-modal object retrieval but cannot produce spatially grounded interaction masks.

In contrast, our work formulates affordance detection as an instruction-conditioned segmentation task that enables open-vocabulary, multi-step reasoning directly over 3D point clouds, overcoming the rigidity of fixed labels and the limitations of prior semantic alignment methods.

2.2 Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) extend the language understanding capabilities of LLMs to the visual and spatial domains [16, 17] by aligning textual tokens with visual and geometric inputs. Initial breakthroughs in 2D MLLMs [18-20] enabled joint reasoning over images and text, yet these models lacked the granularity necessary for fine-grained visual tasks such as segmentation. To mitigate this, models like LLaVA [21] introduced spatial localization, improving regional understanding.

Inspired by this progress, researchers have begun extending MLLMs to the 3D domain. Objectcentric MLLMs such as PointLLM [22] and ShapeLLM [23] utilize point-based encoders and

multi-view distillation to encode geometric structure and semantics. These models demonstrate strong performance in 3D captioning and object-level referring expression grounding, but often operate on isolated objects and fail to model complex inter-object spatial dependencies. Scenelevel LMMs such as Chat-3D [24], LL3DA [25], and 3D-LLM [26] extend grounding to richer indoor scenes using object identifiers, positional embeddings, and pre-selection modules to facilitate dialogue-driven scene understanding.

However, despite these advances, existing 3D MLLMs predominantly focus on global grounding and object identification, lacking the capacity for localized, affordance-specific segmentation or functional reasoning over object parts. 3D-AffordanceLLM [6] takes a step forward by introducing an <AFF> token and a custom decoder to generate affordance masks from natural language queries. Unlike [6], which performs single-pass instruction-to-mask mapping with no feedback loop, our method introduces an Iterative Self-Prompting mechanism that progressively refines predictions by leveraging prior affordance masks as feedback prompts. Moreover, 3D-AffordanceLLM lacks any geometric introspection; in contrast, our Differential Geometric Self-Prompting explicitly uses curvature, Laplacian, and boundary topology cues for precise localization, going beyond language-only guidance. While 3D-AffordanceLLM relies on a fixed decoder architecture, our Multi-scale Visual-Language Integration Module dynamically aligns instructions with geometric features at varying resolutions, enhancing affordance prediction across objects of diverse scale and complexity. Finally, unlike their static segmentation output, our Affordance Dictionary Adaptive Fusion fuses temporal and functional context across steps, enabling robust multi-stage affordance reasoning. Together, these advances allow our model to achieve superior open-world generalization and performance in sequential, instruction-grounded 3D affordance tasks.

3 Proposed Method

We propose ViSPLA , a novel iterative self-prompting framework for language-guided 3D affordance detection that incorporates differential geometric feedback for progressive mask refinement. Unlike previous methods that rely on single-pass inference, our approach employs a recurrent self-prompting mechanism that leverages the intrinsic geometric properties of predicted affordance masks to guide subsequent refinements.

3.1 Probelm Formulation

Following the paradigm reformation introduced by 3D-AffordanceLLM [6], we formulate affordance detection as an Instruction Reasoning Affordance Segmentation (IRAS) task. Given a natural language instruction L and a point cloud P ∈ R N × 3 containing N points, our goal is to predict a binary affordance mask M∈{ 0 , 1 } N indicating regions suitable for the specified interaction. While existing approaches [6] model this as a direct mapping f θ : ( P , L ) ↦→M , we introduce an iterative refinement process, as already described in section 1:

where M 0 = f θ ( P , L ) is the initial affordance prediction and G is our proposed geometric prompt generator that extracts meaningful differential features from previous mask predictions. ViSPLA consists of three main components: (1) a language-guided affordance detection backbone based on 3D-AffordanceLLM, (2) a differential geometry-based self-prompting module, and (3) an iterative affordance refinement module, consisting of a multi-stage refinement decoder, implicit neural field supervision, and spectral convolutional self-prompting. The overall workflow is shown in Figure 2.

3.2 Preliminaries: Language-guided Affordance Detection Backbone

We build upon the 3D-AffordanceLLM [6] architecture, adopting it as our backbone, which comprises a pre-trained point encoder f PE, a point cloud backbone f PB, a projection module f proj, a large language model f LLM, and an affordance decoder f AFD. Given an input point cloud P and a natural language instruction L , the system proceeds as follows: the point encoder first extracts geometric features X = f PE ( P ) ∈ R m × c , where m denotes the number of keypoints and c is the feature dimension. These features are projected into the token space via f proj, yielding Y = f proj ( X ) ∈ R m × d , where d matches the dimensionality of the LLM token embeddings. The projected point tokens are concatenated with the instruction tokens and passed into the LLM f LLM, which processes them

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to generate a response sequence that includes a special affordance token <AFF>. The hidden representation corresponding to <AFF>, denoted as h aff, is then extracted. Meanwhile, the point cloud backbone f PB computes dense point-wise features X ′ = f PB ( P ) ∈ R N × c ′ . Finally, the affordance decoder f AFD fuses h aff with X ′ to produce the initial affordance mask M 0 = f AFD ( h aff , X ′ ) .

3.3 Iterative Differential Geometry-based Self-Prompting

Building upon the initial prediction M 0 , we introduce our core contribution: Iterative Differential Geometry-Based Self-Prompting (IDGSP) . This module leverages geometric feedback derived from prior affordance masks to progressively refine segmentation outputs. At each iteration t , we compute a set of differential geometric descriptors from the previous mask M t -1 :

where ∇ 2 M t -1 denotes the Laplacian of the mask capturing local curvature variation, ∇M t -1 · n represents the normal derivative quantifying alignment with surface normals, H ( M t -1 ) is the mean curvature of mask boundaries, and κ 1 , κ 2 are the principal curvatures. These geometric signals encode essential boundary-aware and topological properties that reflect the physical plausibility of affordance regions. The extracted descriptors are transformed into a dense per-point representation Z t ∈ R N × d using a learnable geometric feature extractor f GFE: Z t = f GFE ( G ( M t -1 )) . This geometry-driven prompt Z t is then injected into the multi-stage refinement decoder (subsubsection 3.4.1) to guide the refinement process.

3.4 Iterative Affordance Refinement

3.4.1 Multi-Stage Refinement Decoder

To operationalize the geometric self-prompting mechanism, we employ a multi-stage refinement decoder that iteratively updates the affordance mask using both language and geometry-informed cues. At each iteration t , the geometric features Z t are fused with the initial LLM embedding h aff using a cross-attention mechanism:

The refined embedding h ( t ) aff is then combined with dense point cloud features X ′ via a mask generation module f MG to produce the updated affordance mask:

3.4.2 Implicit Neural Affordance Field Self-Supervision

To complement the discrete iterative refinement process with a smooth, continuous representation, we incorporate a regularization strategy based on implicit neural fields to enhance boundary precision and geometric consistency without relying on additional labels. This component learns a continuous implicit function Φ ω : R 3 × R d → [0 , 1] , parameterized by ω , which maps any 3D point x ∈ R 3 and its corresponding feature vector to a scalar-valued affordance probability.

The function Φ ω is trained via energy minimization loss L INAFS = E (Φ ω ) over the 3D spatial domain Ω , incorporating geometric priors and alignment with the current mask predictions. The energy term is defined as:

Here, the first term encourages spatial smoothness by minimizing the gradient norm of the implicit field. The second term enforces fidelity to the current predicted mask M ( x ) at the boundary ∂ Ω , ensuring consistency with previously inferred affordances. The third term aligns the second-order variation of the field, measured by the Laplacian ∆Φ ω , with the Gaussian curvature κ ( x ) (where κ = κ 1 · κ 2 ), thereby promoting geometric conformity with intrinsic surface structures. The weighting parameters λ 1 , λ 2 , and scaling constant β balance the contributions of fidelity and curvature alignment. After optimization, the final affordance mask is extracted by thresholding the implicit field at 0.5:

This implicit representation allows the model to refine coarse predictions into geometrically consistent and semantically plausible affordance regions, even in the absence of explicit supervision.

3.4.3 Spectral Convolutional Self-Prompting

To complement spatial refinement with a frequency-aware perspective, we introduce Spectral Convolutional Self-Prompting (SCSP) , which enables the model to capture affordance structures at multiple scales in the spectral domain of point cloud. We treat the 3D point cloud as a discrete manifold encoded by the normalized Laplacian operator L = I -D -1 / 2 AD -1 / 2 , where A is the affinity matrix derived from local geometric similarity, and D is the corresponding degree matrix. Given the predicted affordance mask M∈ R N , we project it to spectral domain via eigen-decomposition:

Here, { u i } N i =1 are the eigenvectors of L , and { α i } are the corresponding spectral coefficients. Refinement is performed by applying a learnable spectral filter g ( λ i ) , parameterized over the eigenvalues { λ i } , yielding the updated mask in the spectral domain:

By operating in the spectral domain, SCSP provides a principled, resolution-aware mechanism for affordance enhancement without explicit hierarchical supervision. The entire refinement process is performed iteratively for T steps, allowing the model to progressively improve affordance localization by incorporating differential geometric feedback.

3.5 Overall Learning Strategy

To effectively address data scarcity and ensure robust affordance understanding, we adopt a multistage training strategy inspired by 3D-AffordanceLLM [6]. The pre-trained backbone is frozen, and we train the proposed self-prompting modules, including IDGSP, INAFS, and SCSP, to refine affordance masks using geometric and spectral cues. Our overall loss combines multitask objectives:

Here L txt is autoregressive CE loss for LLM response generation, L mask is BCE + Dice loss for initial affordance mask prediction, both used in the affordance backbone (following [6]). L IDGSP penalizes

inconsistencies between iterative mask refinements and encourages smoothness, L INAFS denotes energy-based regularization over the implicit affordance field (described in subsubsection 3.4.2), L SCSP performs spectral consistency and spatial regularization using total variation TV . Specifically, L IDGSP and L SCSP are formulated as:

where in Equation 10, || · || 2 W 2 , 2 is the Sobolev W 2 , 2 norm measuring the difference between consecutive masks, capturing both value differences and derivatives (geometric properties), λ t is iteration-specific weight, ∇ 4 is biharmonic operator, and α |∇ 4 M T | 2 2 is Tikhonov regularization term ensuring the final mask has smooth boundaries with controlled curvature. The first term in Equation 11 penalizes changes in the spectral components of the mask across iterations, enforcing frequency-consistent refinement, whereas the second term ensures that the final predicted mask M T is spatially smooth, reducing over-segmentation and promoting contiguous affordance regions. ∑ T t =1 is summation over all iterations of the self-prompting process (from 1 to T ), ∑ K k =1 is summation over K different frequency bands or scales of analysis, W k is diagonal matrix that isolates the k -th frequency band, γ k is scale-dependent weight coefficients for each frequency band k , ˆ M t is spectral decompositions of affordance masks at iteration t , | · | 2 F is Frobenius norm squared, measuring differences in frequency components, τ denotes weight parameter balancing spectral consistency and spatial coherence, and TV ( M T ) is total variation regularizer promoting spatial smoothness in the final mask.

This multi-stage optimization pipeline enables the model to progressively refine affordance predictions by integrating linguistic reasoning with geometric and spectral feedback, leading to improved generalization and mask accuracy in open-world settings.

4 Experiments and Results

4.1 Dataset Description

Following previous works [5, 14], we conduct evaluations on two complementary 3D affordance datasets: PIAD [12] and LASO [14], each designed to test different aspects of generalization. PIAD serves as a complementary benchmark, comprising 7 , 012 point clouds from the same object categories as LASO but introduces a stricter generalization setting-entire object instances are withheld from training, requiring the model to predict affordances on previously unseen geometries. As PIAD lacks textual annotations, we augment it with language instructions by sampling prompts from LASO's question pool, ensuring semantic alignment with each target affordance type. This design enables evaluation of our model's robustness in both instruction-conditioned and shape-driven generalization scenarios. LASO, on the other hand, contains 19 , 751 language-guided point cloud pairs spanning 8 , 434 unique object instances across 23 object categories and 17 affordance types. It supports both Seen and Unseen splits, where the Unseen configuration deliberately excludes specific affordance-object combinations during training to assess zero-shot generalization.

4.2 Implementation Details

Following 3D-AffordanceLLM [6], we utilize Phi-3.5-mini-instruct [27] as our base LLM with LoRA [28] fine-tuning. For 3D processing, we adopt Point-BERT [29] pre-trained with ULIP2 [30] as our point encoder ( f PE ) and Point Transformer [31] as our point backbone ( f PB ). The feature dimension d is set to 512 for both language and point features. The projector layer ( f proj ) is implemented as a simple linear layer

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mapping point features to match the LLM token dimension. For the Affordance Decoder, we follow the architecture from LISA [32] but adapted for 3D data. For our iterative self-prompting mechanism, we set the number of refinement iterations T = 3 (as performance plateaus beyond this point while computational cost rises sharply (see Figure 3)), with weight parameters λ t = 0 . 8 t to gradually reduce consistency constraints. In the IDGSP loss, we set α = 0 . 1 for the Tikhonov regularization term. For INAFS, we use λ 1 = 0 . 5 , λ 2 = 0 . 3 , and β = 0 . 05 . The SCSP module uses K = 3 frequency bands (following validation in Figure 3) with weights γ 1 = 1 . 0 , γ 2 = 0 . 7 , γ 3 = 0 . 4 , and τ = 0 . 2 for the total variation term. We use AdamW optimizer with an initial learning rate of 4 × 10 -5 with cosine scheduling and warm-up ratio of 0 . 03 . All experiments are done on four NVIDIA V100 GPU with a batch size of 16 , training for 20 epochs in ∼ 12 hr .

4.3 Findings and Comparison with SoTA

Our proposed framework achieves consistent and substantial performance improvements across the PIAD benchmark, as shown in Table 1, setting a new stateof-the-art for language-guided 3D affordance detection. To ensure a fair and consistent comparison, we reproduced the results of 3D-AffordanceLLM [6] under our evaluation protocol and dataset setup, accounting for differences from the original implementation. In the seen configuration, our method achieves 23.1% aIoU, 85.8% AUC, and 0.664 SIM, outperforming the prior best (GEAL) by relative 2.66%, 0.92%, and 10.48%, respectively. In the unseen split-designed to evaluate zero-shot generalization to novel affordance-object pairs-we obtain 9.2% aIoU, 73.1% AUC, and 0.431 SIM, again surpassing GEAL by 0.5% in aIoU, 0.8% in AUC, and 4.1% in SIM. This improvement reflects our framework's ability to preserve spatial and structural coherence in predicted masks, particularly in ambiguous or underrepresented regions. Earlier fusion-based approaches like [33-38] exhibit significantly inferior performance due to their generic multimodal architectures that fail to model the specialized nature of affordance relationships. These methods are unable

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to bridge the geometric-semantic gap, resulting in substantial performance degradation (relative aIoU dropping by more than 50% compared to our method). Unlike prior methods such as [14, 6, 5] that operate in a single-pass decoding mode and rely heavily on textual embeddings, our method incorporates closed-loop refinement with geometric feedback, allowing it to resolve fine-grained boundaries in a context-aware manner.

Similar trends are observed on the LASO dataset, where our model achieves 22.8% aIoU, 87.3% AUC, and 0.651 SIM on seen objects, and 17.1% aIoU, 81.5% AUC, and 0.571 SIM in the more challenging unseen setting. Notably, in the unseen split, our framework outperforms the best baseline (GEAL) by 2.4% aIoU, 0.9% AUC, and 0.8% SIM relative. Traditional baselines such as [12, 26, 14] suffer huge degradation in performance when transitioning from seen to unseen due to their rigid, non-adaptive architectures. Even stronger models like [6, 5] exhibit sharp performance drops (e.g., GEAL: 22.0 → 16.7 aIoU on PIAD), revealing their limited ability to transfer learned affordance priors to unfamiliar topologies.

4.4 Ablation Study

The ablation study in Table 2 demonstrates the incremental contribution of each component in our self-prompting framework. When comparing the baseline (row 1) to the full model (row 4), we observe consistent performance improvements across all metrics and datasets, with particularly significant gains in the unseen settings.

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(1) The Spectral Convolutional Self-Prompting (SCSP) module provides the initial performance lift (+0.6/+1.3 aIoU on PIAD/LASO seen), confirming the effectiveness of frequency-domain processing for capturing multi-scale affordance patterns. By operating in the spectral domain, SCSP enables the model to modulate signal components at different structural frequencies, processing both coarse affordance regions and fine boundary details simultaneously (shown in rows 1, 2 of Figure 4). (2) Adding Implicit Neural Affordance Field Self-Supervision (INAFS) yields further improvements (+0.8/+0.8 aIoU), particularly in structural similarity metrics. This suggests that the continuous implicit field representation enhances boundary precision and produces geometrically coherent affordance regions. The implicit field's ability to capture smooth transitions and model topological relationships proves especially beneficial for complex object geometries, as evident in row 3 of Figure 4. (3) The most substantial gains come from incorporating Iterative Differential Geometry-Based Self-Prompting (IDGSP) , which provides a significant boost on LASO seen (+2.5 aIoU) and notable improvements across unseen scenarios. This demonstrates that leveraging geometric features (Laplacians, curvatures, normal derivatives) as visual prompts enables the model to progressively refine affordance boundaries through geometric feedback, particularly crucial for distinguishing fine-grained functional regions (row 4 of Figure 4). The synergistic effect of all three components is most pronounced in generalization scenarios, where the relative improvements are larger for unseen settings than seen settings. This confirms our hypothesis that geometric self-prompting enhances the model's ability to adapt to novel object-affordance relationships by leveraging intrinsic geometric cues rather than relying solely on seen training examples. Qualitative visualization is provided in Figure 5. Additional findings can be found in the supplementary file.

4.5 Cross-dataset Generalization

Table 3 highlights the cross-dataset generalization performance when models trained on LASO are evaluated on PIAD. Our full model outperforms prior state-of-the-art (GEAL) across all metrics, achieving 19.7%/12.5% aIoU, 84.5%/75.2% AUC, and 0.610/0.465 SIM in seen/unseen splits-marking up to +1.3% aIoU and +0.025 SIM improvements.

The ablation variant without SCSP shows a clear drop (-0.8% seen, -0.7% unseen aIoU), confirming SCSP's role in learning transferable, multi-scale affordance cues. Even without it, our model still outperforms [5, 6], validating the strength of our differential geometrybased self-prompting. All methods exhibit performance degradation in unseen

scenarios, but our model maintains the smallest drop, indicating greater robustness to distribution shifts-enabled by shape-aware reasoning rather than dataset-specific memorization.

5 Conclusion and Future Works

We presented ViSPLA , a geometry-aware iterative framework for language-guided 3D affordance detection. By combining differential geometric self-prompting, implicit neural fields, and spectral refinement, our model progressively improves affordance segmentation beyond the constraints of single-pass or fixed-label paradigms. Experimental results on LASO and PIAD benchmarks show strong generalization, particularly in zero-shot and cross-dataset settings. Despite its strengths, ViSPLA incurs additional computation due to iterative refinement and may face challenges with highly deformable or articulated objects. Future work will explore hybrid prompting strategies that combine geometric and learned latent cues, as well as adaptive iteration control for real-time efficiency. Extending to dynamic scenes and scene-level affordance reasoning is another promising direction. Together, these developments will move us closer to robust, generalizable affordance understanding in complex real-world environments-paving the way for more capable and adaptable embodied agents.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ViSPLA, an iterative self-prompting framework for language-guided 3D affordance prediction. The method redefines affordance reasoning as a language-conditioned segmentation task over point clouds and introduces a series of geometric modules—including INAFS and SCSP—to progressively refine affordance masks. INAFS imposes continuous geometric priors via neural fields, while SCSP operates in the spectral domain to support multi-scale refinement. Experiments on LASO and PIAD benchmarks demonstrate that ViSPLA achieves sota results.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n1.This paper proposes an iterative refinement strategy that achieves strong results with a small number of iterations (T = 3), demonstrating a good trade-off between performance and efficiency.\n\n2.By incorporating geometric priors, this paper introduces loss functions that are better aligned with affordance detection, enabling more accurate mask refinement.\n\n3.The proposed method achieves sota performance on multiple affordance datasets, validating its effectiveness.\n\n**Weaknesses:**\n\n1.My score is not high because I think the contributions of this work are not fully explored. Specifically, the proposed modules (INAFS and SCSP) lack in-depth analysis, which makes them appear somewhat incremental. I suggest the authors provide more comprehensive quantitative and qualitative evaluations of both modules. For example, why does frequency-aware convolution help in capturing affordance structures? Does it work similarly to [1]?\n\n2.For IDGSP, a runtime analysis of the computational cost of refinement iterations would be helpful to justify the efficiency of the iterative framework.\n\n3.I recommend including a w/o ablation study on IDGSP, INAFS, and SCSP, to better isolate the contribution of each component.\n\n4.Since INAFS and SCSP function as general-purpose geometric regularizers for affordance detection, could they be directly used to fine-tune 3D affordance-aware LLMs?\n\nQUESTIONS:\nOverall, it is an interesting paper. I hope the authors address/clarify the questions raised in the weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ViSPLA, a novel framework designed to enhance language-guided 3D affordance prediction. Different from existing methods that often rely on fixed categories or external prompts, ViSPLA employs an iterative self-prompting mechanism from the initial estimation. This mechanism refines predicted affordance masks by leveraging intrinsic geometric feedback, including Laplacians, normal derivatives, and curvature fields, encoded as visual prompts. Key components include Implicit Neural Affordance Fields for continuous probabilistic region definition and a Spectral Convolutional Self-Prompting (SCSP) module for multi-scale refinement in the frequency domain. The framework redefines affordance detection as a language-conditioned segmentation task, demonstrating state-of-the-art results on both seen and unseen objects across two benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- The idea of using the iterative self-prompting mechanism is a well-motivated and powerful concept. This allows the model to self-correct and progressively improve its predictions, leading to more accurate and geometrically consistent results, as strongly supported by the ablation studies.\n\n- The technical contributions of this paper are rich. For instance, the usage of implicit representation allows the model to refine coarse predictions into geometrically consistent and semantically plausible affordance regions without relying on additional labels. In addition, the proposed SCSP module enables the multi-scale analysis in the frequency domain.\n\n- Strong performance.  ViSPLA achieves significant improvements over previous methods on two challenging benchmarks. The evaluation is thorough, including performance on \"seen\" and \"unseen\" object-affordance pairs, cross-dataset generalization, and a detailed ablation study that demonstrates the contribution of each component of the framework.\n\n## Weakness:\n\n- Concerns about the efficiency. This paper proposes an iterative method, where each refinement step increases the inference time. It is important to report the inference time and compare it with the single-stage approaches.\n\n- Reliance on a Pre-trained Backbone: ViSPLA builds upon an existing model, i.e., 3D-AffordanceLLM, as its backbone for initial prediction.  The overall performance of ViSPLA is still dependent on the quality and capabilities of the initial result. I'm curious about the robustness of the refinement module to the initial estimation.\n\nQUESTIONS:\nPlease refer to the weakness section.\n\nLIMITATIONS:\nThe authors have discussed the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ViSPLA, a novel framework for language-guided 3D affordance prediction. The key innovation lies in an iterative self-prompting mechanism that leverages differential geometric cues from previously predicted masks to guide subsequent refinement. The model comprises three key modules: (1) Iterative Differential Geometry-based Self-Prompting (IDGSP), (2) Implicit Neural Affordance Field Supervision (INAFS), and (3) Spectral Convolutional Self-Prompting (SCSP). Extensive experiments on LASO and PIAD benchmarks show that ViSPLA achieves state-of-the-art results, particularly in generalization to unseen affordance-object pairs and cross-dataset settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n- The paper clearly formulates the problem as an Instruction Reasoning Affordance Segmentation (IRAS) task and provides a detailed breakdown of the proposed architecture and its components, making it relatively easy to follow.\n- The paper includes comprehensive ablation studies isolating the contributions of each module (IDGSP, INAFS, SCSP), and clearly shows their cumulative impact on performance. \n\nWeakness:\n\n- Figure 3 suffers from visual misalignment and appears manually stretched or distorted. This detracts from the professionalism of the paper \n- While success cases are visualized, the paper would benefit from providing more detailed analysis of typical failure cases(more highly articulated objects). \n- Although the method is well-motivated from an embodied AI perspective, the paper does need evaluate how the predicted affordance masks impact downstream applications like robotic grasping or manipulation.\n\nQUESTIONS:\nThe paper proposes a novel and well-structured framework with strong empirical results and detailed ablations. However, the lack of downstream task evaluation, limited analysis of failure cases, and presentation issues (e.g., Figure 3) weaken its overall impact. I would consider raising the score if the authors provide thorough responses and additional evidence addressing these concerns.\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ViSPLA, a novel framework for language-guided 3D affordance prediction. The task involves identifying functional regions (affordances) on 3D objects based on natural language instructions, which is an important capability for embodied agents navigating complex environments.\n\nThe key idea of the work is to frame affordance detection as a language-conditioned segmentation problem in 3D point clouds. Unlike previous approaches that rely on fixed affordance categories or external expert prompts, the authors introduce an iterative self-prompting mechanism that leverages differential geometric feedback to refine affordance masks progressively. This allows the model to adaptively correct its predictions by exploiting the intrinsic geometry of the predicted surfaces.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) The proposed ViSPLA introduces an innovative iterative refinement mechanism that leverages geometric feedback from predicted masks, enabling progressive self-correction and improved performance over single-pass methods.\n2) The introduction of geometry-based cues could benefit the precise prediction of affordance masks, which is a novel and interesting direction where few works have attempted in this domain.\n3) From both qualitative and quantitative justifications, especially from the visualizations, the proposed method demonstrates a clear improvement in segmentation quality and accuracy.\n\nWeaknesses:\n1) Despite the novel method, the incurred inference cost appears to be quite high, which may limit its applicability in more realistic, time-sensitive scenarios compared to the level of accuracy improvement it offers.\n2) The motivation behind each component should be more clearly stated. For example, both Section 3.3 and Sec3.4 mention the idea of a refinement process. It remains unclear whether it is necessary to apply the refinement technique in two seemingly similar stages.\n3) The proposed method still relies on a supervised setting, which may limit its scalability to completely unseen or out-of-domain 3D objects. However, this is a common challenge across the entire domain.\n\nQUESTIONS:\n1) What is the inference cost compared with previous method? There is no such analysis in the main manuscript.\n2) Why did you add an AFF token to decode the segmentation mask? Did you try using the output of the language decoder as a prior to decode the mask? This might provide more informative guidance for extracting a precise affordance mask.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-07"}