ViSPLA: Visual Iterative Self-Prompting for Language-Guided 3D Affordance Learning
Authors: Hritam Basak
Zhaozheng Yin
Venue: NeurIPS 2025 poster ()
Keywords: Robotics
Affordance Estimation
Vision-language Model
Primary area: other

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
Hritam Basak
Zhaozheng Yin Department of Computer Science
Department of Computer Science Stony Brook University Stony Brook, NY, USA
Stony Brook University Stony Brook, NY, USA hord@ccsstonbbbook
hbasakkcs. stw.endooosstttooooooooooooo edu
Abstract
We address the problem of language-guidee 3D affordance prediction, core capability for embodied agents interacting with unstructured environments. Exist- ing methods often rely fixed affordance categories require external expert prompts, limiting their ability to generalize across different objects and interpret multi-step instructions. In this work, introduce VISPLA, novel iterative self- prompting framework that leverages the intrinsic geometry predicted masks for continual refinement. We redefine affordance detection segmentation task: given a 3D point cloud and language instruction, our model pre- dicts sequence of refined affordance masks, each guided by differential geometric feedback including Laplacians, normal derivatives, and curvature fields. This feed- back is encoded into visual prompts that drive multi-stage stage refinement decoder, enabling the model to self-correct and adapt to complex spatial structures. To further enhance precision and coherence, introduce Implicit Neural Affordance Fields, which define continuous probabilistic regions over the 3D surface without additional supervision. Additionally, our Spectral Convolutional Self- Soff-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-e results on both seen and unseen objects on two benchmark datasets. Our framework establishes a new paradigm for open-works 3D affordance reasoning by unifying language comprehension with low-level geometric perception through iterative refinement. Project Website
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Introduction
Affordance, initially conceptualized by Gibson [1], defines the potential action possibilities that objects present to an agent. The evolution robotic systems toward increasingly unstructured envi- ronments necessitates fundamental paradigm shift in how we conceptualize affordance detection. Formally, we can represent the affordance detection problem as mapping function fo (P) + A, where P E RR-x3 denotes 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 limited set of predefined K affordance categories A {ax restricts the generalization capability and operational flexibility in dynamic, real-word woal-world environments [2]. Although conventional methodologies have predominantly focused on visual modalities, attempt- ing 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
{1, 2, K). which fundamentally
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a)
Grasp
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P
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fo
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Push
K
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(c)
(cF)( IDGSP
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Ileaative Diteremital
IDOSP: Geometry-Sased
fo
Swif- Swil-Phompting
INAFS
fMG
INAFS:
Affordance
"Locate areas
SCSP
Spectall
MT
SCSP: Convelutional
grasping"
Iterative Affordance Refinement
Self Prompting
Figure 1: (a) Traditional vision- methods [3, 4] rely trainable network fo to predict fixed set of affordances fo + A: AA {ai, 42 [0x] }: (b) Language input along with point-cloud add more flexibility comprehend complex language instructions and mitigate the problem of open-set affordance prediction [5, 6]: So (P. L) + .M; (c) We propose refining the initial affordance prediction Ma for 7 steps using our proposed IDGSP, and Iterative Affordance Refinement module, consisting of multi-stage refinement decoder fMG. INAFS (#w) and SCSP. Our solution could be formulated as fa (P, (P. C) Mú; {1, ..T). Details can be found section
limitation that inhibits the deployment autonomous agents real-world contexts. Language-guided affordance prediction offers mathematically elegant solution to this complex problem.
Language-guided affordance detection from 3D point clouds represents pivotal direction em- bodied 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 more generalizable mapping: Fo (P, f) + M. where & represents the learnable parameters, L represents a linguistic instruction, and M E {0, 1) is the binary affordance mask (visualized in Figure 1(b)). This formulation opens avenues for handling more diverse and complex scenarios potentially allow- ing 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 sequential reasoning and knowledge grounding [7]. but these models often decoupled from 3D perception. Mcanwhile, 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 poopp-roomtigg-bbbe based 3D affordance detection paradigm that bridges the gap between language understanding and affordance segmentation through geometric feedback-drive
refinement, as shown in Figure Icc). Unlike prior approaches that perform single- pass inference, our method implements closed-loop system where each predicted affordance mask is used to generate geometric self self-prompts that refine subsequent predictions. Mathematically. we formulate this as: M, fo (P, G(M,-1), L); t 2,. ...,..'). T', where Mo fa C) is the initial decoder, and G denotes the geometric prompt
affordance mask predicted from a language-condition generator that extracts differential features (c.g., curvature, normal derivatives) from M, The final refined mask My 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
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

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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- Soff-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- eelf-impooiing cycle that enhances precision across multiple object geometries.
We propose a novel Differential Geometric Self- Prompting mechanism that extracts math- ematical 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 fea- tures, 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 en- force geometric consistency, even without extra supervision. In tandem, our Spectral Convolutional Self-Prompting module analyzes and enhances affordance predic- tions at multiple structural scales, enabling the model to capture both broad shapes and fine details for robust and accurate segmentation, especially in sparse noisy scenarios.
We demonstrate that fine-tuning pre-trained MLLMs through our self- seff-prompting framework yields superior performance both seen and unseen scenarios.
2 Related Work
2.1 Affordance Detection
Affordance detection aims to identify functionally interactive regions objects, crucial for enabling robotic agents to manipulate and reason about the physical world. Early works in 2D explored object- level affordances using CNNs [3], later extending to Iagguag-cconitiinne 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 -baee 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 cross-modal object retrieval but cannot produce spatially grounded interaction masks.
In contrast, our work formulates affordance detection as an instruction-conditiones 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-graing 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 MILMS to the 3D domain. Object- centric MLLMs such PointLLM [22] and ShapeLLM [23] utilize point-based encoders and
3

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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. Scene- level LMMs such as Chat-3I [24], LL3DA [25], and 3D-LLMM [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, infordance-speciffe segmentation functional reasoning over object parts. 3D- -o-AfoordaaceLLMM [6] takes a step forward by introducing an <AFF> token and a custom decoder generate affordance masks from natural language queries. Unlike [6], which performs single-pas pass instruction-to taakuction-to-maak mapping with no feedback loop, our method introduces an Iterative Prom-Proopting mechanism that progressively refines predictions by leveraging prior affordance masks as feedback prompts. Moreover, 3ID-AffordnccelJJ LLM 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-AffordanceLL.M 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, 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-gre -rruunded 3D affordance tasks.
Proposed Method
We propose VISPLA, novel iterative self- self-prompting framework for danguage-guide 3D affordance detection that incorporates differential geometric feedback for progressive mask refinement. Unlike previous methods that rely on single-pass inference, approach employs a recurrent self- cel-pproppingg mechanism that leverages the intrinsic geometric properties predicted affordance masks to guide subsequent refinements.
3.1 Probelm Formulation
Following the paradigm reformation introduced by LD [6], we formulate affordance detection as Instruction Reasoning Affordance Segmentation (IRAS) task. Given natural language instruction and point cloud P E R containing points, our goal to predict a binary affordance mask M E {0, 1 indicating regions suitable for the specified interaction. While existing approaches [6] model this as a direct mapping fe (P, + M, we introduce an iterative refinement process, already described in section 1:
M. = fo = (P)9(M:))); E
T}
where Mo fo is the initial affordance prediction and G is proposed geometric prompt generator that extracts meaningful differential features from previous mask predictions. VISPLA consists of three main components: (1) language-guides guided affordance detection backbone based on 3DDAAffordanceeLMMM (2) a differential geometry-based self-prompting module, and (3) an iterative affordance refinement module, consisting of 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-AffordanceLLMM [6] architecture, adopting it our backbone, which comprises a pre-trained point encoder frE. a point cloud backbone frg. projection module fproj. a large language model fLLM. and affordance decoder fAFD. Given input point cloud P. and natural language instruction L, the system proceeds as follows: the point encoder first extracts geometric features X (P) Rmncc where denotes the number of keypoints and is the feature dimension. These features are projected into the token space via fpmj. yielding Y = fpmn(() X)) E IRmxd where matches the dimensionality of the L.M token embeddings. The projected point tokens are concatenated with the instruction tokens and passed into the LLM fULM which processes them

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Figure 2: Overview of the VISPLA framework: given point cloud P and a language instruction C, first extract geometrie features X fPE (PE(P), project them to Y fproj (X), and pass them along with language tokens to the frozen LLM with trainable LoRA layer. Next, dense point-cloud features X' f (P) and affordance tokens from LLM haff fLLM (Y, C) are extracted and passed to the affordance decoder SArD to produce an initial mask Mo- The iterative affordance refinement module then refines the mask via T steps. At each iteration geometric descriptors Z₁ far E (G(M)))) (e.g., Laplacian, curvature) are computed (subsection 3.3) and injected as visual prompts, along with X' and faff multi-stage refinement decoder JMG (subsubsection 3.4.1) to produce the refined affordance prediction Mr, after processing them through INAFS (subsubsection 3.4.2) and SCSP (subsubsection 3.4.3). The process converges after T steps, enabling precise, language-guides 3D affordance segmentation My through closed-loop geometric feedback.
to generate a response sequence that includes a special affordance token <AFF>, The hidden representation corresponding to <AFF>, denoted as han is then extracted. Meanwhile, the point cloud backbone frn computes dense point-wise features X' frn (P) RNxe', Finally, the affordance decoder SAND fuses har with X' to produce the initial affordance mask Mo JArD (har, X').
3.3 Iterative Differential Geometry-based Self-Prompting
Building upon the initial prediction Ma. introduce core contribution: Iterative Differential Geometry- Based reff-Prompting (IDGSP). This module leverages geometric feedback derived from prior affordance masks progressively refine segmentation outputs. At each iteration I, we compute a set of differential geometric descriptors from the previous mask M
((M--1) (V VM,-1 M(M,-1), (2(Mi11). (2(M))))
(2)
where V V²M,- denotes the Laplacian of the mask capturing local curvature variation, VM represents the normal derivative quantifying alignment with surface normals, H(M)-1) is the mean curvature of mask boundaries, and K1, K2 are the principal curvatures. These geometric signals encode essential boundary-
and topological properties that reflect the physical plausibility of affordance regions. The extracted descriptors are transformed into a dense per-point representation Z, R using learnable geometric feature extractor farE: 2, fAre (G(Mr-1)). This geometry-driver prompt 2, is then injected into the multi-stage refinement decoder (subsubsection 3.4.1) guide the refineeent 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-informet cues. At each iteration f, the geometric features Z, are fused with the initial LLM embedding hat using cross-attention mechanism:
h aft
CrossAttn(hati Z,)
The refined embedding (1 is then combined with dense point cloud features X' via mask generation module fMG to produce the updated affordance mask:
M, (h X')
(4)
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3.4.2 Implicit Neural Affordance Field Self-Supervision
To complement the discrete iterative refinement process with smooth, continuous representation, we incorporate 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 &., : R x IRd - [0, 1]. parameterized by w, which maps any 3D point x R and its corresponding feature vector to scalar-valued affordance probability.
The function &w is trained via energy minimization loss LINAFS E(ww) over the 3D spatial domain Ω incorporating geometric priors and alignment with the current mask predictions. The energy term is defined as:
E(Dw) - [IVe [
I (x)( M(x))² dx dxx do (xA00(
31]x(x)11)* dx
(5)
Here, the first term encourages spatial smoothness by minimizing the gradient norm the implicit field. The second term enforces fidelity to the current predicted mask MM() at the boundary an, ensuring consistency with previously inferred affordances. The third term aligns the second-order variation of the field, measured by the Laplacian A with the Gaussian curvature x(x) (where == Ki K2), thereby promoting geometric conformity with intrinsic surface structures. The weighting parameters λT X2, and scaling constant & balance the contributions of fidelity and curvature alignment. After optimization, the final affordance mask is extracted by thresholding the implicit field at 0.5:
Myelined = {x E P Dh (x) 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 frequency lutional 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 discrete manifold encoded by the normalized Laplacian operator L = I D D-1122 AD 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 E IR-V, we project it to spectral domain via eigen-decomposition:
perspective, we introduce Spectral Convo-
N
M
a where a; = (M,u,) u,)
Here, [ are the eigenvectors of L, and {as) are the corresponding spectral coefficients. Refine- ment is performed by applying a learnable spectral filter g(),), parameterized over the eigenvalues {Ar), yielding the updated mask in the spectral domain:
M Σ g(a))a
(8)
By operating in the spectral domain, SCSP provides a principled, resolution-awart 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 multi- stage training strategy inspired by AD-AffordanceLL... [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:
L AitC Amask XmnGsPLIGSPP + AINAFSCINATS + AscsP
Here LIs is autoregressive CE loss for L.LM response generation, Lmask is BCE Dice loss for initial affordance mask prediction, both used in the affordance backbone (following [6]). LIDGSP penalizes

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inconsistencies between iterative mask refinements and encourages smoothness, LNAAS denotes energy-base regularization over the implicit affordance field (described subsubsection 3.4.2), LSCSP performs spectral consistency and spatial regularization using total variation TV. Specifically, LIDGSP and Lscsp are formulated as:
LIDGSP
and AeIMM - Me-illia. +a + M
(10)
K
LsCsp
Th WM
Mi-IIl + +TV(MT)
(11)
where in Equation 10, ||. 1liv is the Sobolev W norm measuring the difference between consecutive masks, capturing both value differences and derivatives (geometric properties), X is iteration-specific weight, V is biharmonic operator, and o/V*MT 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-consisteni refinement, whereas the second term ensures that the final predicted mask Mr is spatially smooth, reducing wver-segmentttion and promoting contiguous affordance regions. Σi=1 summation over all iterations of the self- self-prompting process (from to T), Σ is summation over K different frequency bands or scales of analysis, Wi is diagonal matrix that isolates the k-th frequency band, "/h: is scale-dependent weight coefficients for each frequency band k; MM, is spectral decompositions affordance masks iteration 4, 1/ is Frobenius norm squared, measuring differences in frequency components, denotes weight parameter balancing spectral consistency and spatial coherence, and TV(Mr) is total variation regularizer promoting spatial smoothness in the final mask.
This suati-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.
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 stricter generalization setting- object instances withheld from training, requiring the model to predict affordances on previously unseen geometries. As PIAD lacks textual annotations, from LASO's question pool, ensuring semantic alignment with each target affordance type. This design enables evaluation of our model's 's robustness in both instruction-condiiionedd and shape-driven generalization scenarios. LASO, on the other hand, contains 19, 751 language-guidee point cloud pairs spanning 8, 434 unique object instances across 23 object categories and 17 affordance types. supports both Seen and Unseen splits, where the Unseen configuration deliberately excludes specific affordance-object combinations during training to assess zero-shot generalization.
augment it with language instructions by sampling prompts
4.2 Implementation Details
Following 3D-AffirddanceLLM [6], we utilize Phi-3.5-mini-instree [27] as our base LLM with LoRA [28] fine-tuning For 3D processing, we adopt Point-BEr BERT-BERTTAA [29] pre-trained with ULIP. 2 [30] as our point encoder (frg) and Point Transformer [31] as our point backbone (Srn). The feature dimension d is set to 512 for both language and point features. The projector layer (fproj) is implemented simple linear layer
20
LA80
Figure 3: Performance analysis (aloU on "seen" setting) with varying T' and K values.
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Table 1: Qualitative comparison of our proposed method on the PIAD (left) and LASO (right) datasets. The best and second best results are highlighted in red and blue, respectively. LASO* indicates reported results of LASO [14] in GEAL [5]. denotes our reproduced results of [6].
Type
Method
aloUt
A
SIM
MAF.
MBDF [33]
9.3
74,9
0.415
0.143
PMI [34]
10.1
75.1
0.425
0.141
Type
Method
aloU
AUC
SIM
MAE
FRONN
0,136
Refer RoferTrans
13.7
79.8
0.497
0.124
ILN [36]
11.5
75.8
0.427
0.137
RcLA [40]]
15.2
78.9
0.532
0.118
Prasion [37]
12.3
77.5
0.432
0.135
3D-SPS [41]
11.4
76.2
0.433
0.138
XMF
17.8
LAGNet [12]
20.5
84,9
0.545
0.098
Seen
LASO [14]
20.8
87.3
0.629
0.093
LASO [14]
19.7
84.2
0.590
0.096
LASO" [14]
19.7
85.2
0.600
0.057
3DABILLM
82.6
3DAAILLM' [6]
GEAL [S]
22.5
85.0
0.601
0.092
GEAL [S]
22.0
86.7
0.634
0.092
Ours
23.1
85.8
0.664
0.089
Ours
22.8
87.3
0.651
0.090
MBDF [33]
4.2
58.2
0.325
0.213
ReferTrams [39]
10.2
69.1
0.432
0.145
PMF [34]
4.7
60.3
0.330
0.211
Rel.A [40)
10.7
69.7
0.429
0,144
FRONN
[411
7.9
ILN [36]
4.7
59.7
0.325
0.207
IAGNet [12]
12.9
77.8
0.443
0.129
Prasion [37)
5.3
61.9
0.33
0,193
Unszen
LASO [14]
14.5
80.2
0.507
0.119
Unseen
XMF
62.6
LASO" [14]
15.6
JAGNEt [121
71.8
0.352
0.127
3DAILLM [6]
15.3
78.7
0.542
0.124
LASO [14]
8.D
69.2
0.386
0.118
GEAL
16.7
80.9
0.567
0.106
3DAITLMM'
GEAL [5]
8.7
71.0
72.5
0.390
0.102
Ours
17.1
81.5
0.571
0.163
Ours
73.1
0.431
0.099
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- eeffprrop 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 X 0.8 to gradually reduce consistency constraints. In the IDGSP loss, we set a 0.1 for the Tikhonov regularization term. For INAFS, we use \ 0.5, A2 0.3, and / 0.05. The SCSP module uses K frequency bands (following validation in Figure 3) with weights 71 1.0, 72 0.7, 73 0.4, and т 0.2 for the total variation term. We use AdamW optimizer with an initial learning rate of 4 x 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 12hr.
4.3 Findings and Comparison with SoTA
GT
GEAL
Ours
Our proposed framework achieves consistent and sub- stantial performance improvements across the PIAD benchmark, as shown in Table 1, setting new state- of-the-art language-guns guidedd 3D affordance detec- tion. To ensure a fair and consistent comparison, we reproduced the results of 3D-AffoordanceLLM [6] under evaluation protocol and dataset setup, accounting for differences from the original imple- mentation. In the achieves 23.1% aloU, 85.8% AUC, and 0.664 SIM, outperforming the prior best (GEAL) by relative 2.66%, 0.92%, and 10.48%, respectively. In the un- seen spsit-designed to evaluate zero-shot general- ization to novel affordance-object pairs-we obtain 9.2% aloU, 73.1 % AUC, and 0.431 SIM, again sur- passing GEAL by 0.5% in aloU, 0.8% in AUC, and 4.1% in SIM. This improvement reflects our frame- work's ability to preserve spatial and structural coher- ence in predicted masks, particularly in ambiguous or underrepresented regions. Earlier aision-based ap- proaches like [33-38] exhibit significantly inferior performance due to their generic multimodal archi- tectures that fail to model the specialized nature affordance relationships. These methods are unable
'If you look at the computer displey, which points will you look at'
configuration
method
"Point ideal displaying"
"If you want to ensure the trash can doeen't get damaged, what part would you open"
"Your proferred peen-point dishwasher"
Figure 4: Qualitative comparison of our affor- dance segmentation results with GEAL [5].
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to bridge the ceometrie-semantie gap. resulting in substantial performance degradation (relative aloU dropping by more than 50% compared to our method). Unlike prior methods such as [14, 6, 5] that operate in single-pass decoding mode and rely heavily on textual embeddings, our method incorporates closed-loop refinement with geometric feedback, allowing it to resolve fine-graine fine-grained boundaries in a context-aware manner.
Similar trends are observed on the LASO dataset, where our model achieves 22.8% aloU, 87.3% AUC, and 0.651 SIM on seen objects, and 17.1% % aloU, 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% aloU, 0.9% AUC, and 0.8% SIM relative. Traditional baselines such as [12, 26, 14] suffer huge degradation performance when transitioning from seen to unseen due their rigid, non-adaptive architectures. Even stronger models like [6, 5] exhibit sharp performance drops (e.g.. GEAL: 12.7-16.7 aloU on PIAD), revealing their limited ability to transfer learned affordance priors to unfamiliar topologies.
4.4 Ablation Study
The ablation study in Table demonstrates the incremental contribution of each component in our self-pro self-prompting framework. When comparing the baseline (row 1) to the full model (row 4), we observe consistent performance improvements across metries and datasets, with particularly significant gains in the unseen settings.
(1) The Spectral Convolutional Self-Prompting (SCSP) module provides the initial performance lift (+0.6/+1.3 aloU on PIAD/LASO seen), confirming the effectiveness of frequency-domall processing for capturing multi-scale affordance patterns. By op- erating in the spectral domain, SCSP enables the model to modulate signal components at different structural frequencies, processing both coarse affor- dance regions and fine boundary details simultane- ously (shown in rows 1, 2 of Figure 4). (2) Adding Im- plicit Neural Affordance Field Self-Supervviion (IN- AFS) yields further improvements +008/+0.8 aloU), particularly in structural similarity metrics. This sug- gests that the continuous implicit field representation enhances boundary precision and produces geometri- cally coherent affordance regions. The implicit field's ability to capture smooth transitions and model topo- logical 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-Baa -assed Self- Prompting (IDGSP), which provides a significant boost on LASO seen (+2.5 aloU) 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 geo- metric feedback, particularly crucial for distinguishing fine fine-grained functional regions (row 4 of Figure 4). The synergistic effect of all three components most pronounced in generalization scenarios, where the relative improvements are larger for unseen settings than seen settings. This confirms our hypothesis that geometric self- 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 provided in Figure 5, Additional findings can be found in the supplementary file.
Ours wio IDOSP
Ours wio INAFS
Ours wio SC3P
Ours (full model)
'Where would you grasp the mug and what makes you choose that part?"
"Ideal seat on chair"
Figure 5: Qualitative visualization of ablation experiments.
4.5 Cross-dataset Generalization
Table 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% aloU, 84.5% AUC, and 0.610/0.465 SIM in seen/unseen splits-marking up to +1.3% aloU and +0.025 SIM improvements.

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Table Ablation study of different components. The best results are in bold.
LASO
Type
IDGSP
INAFS
SCSP
aloU
AUC
SIM
MAE
aloU
AUC
SIM
MAE
X
X
21.5
82.6
0.643
0.104
18.2
84,9
0.622
0.104
Seen
X
X
22.1
83.5
0.650
0.099
19.5
85,4
0.631
0.099
22.9
84,2
0.657
0.093
20.3
86.1
0.643
0.094
/
V
23.1
85.8
0.664
0.089
22.8
87.3
0.651
0.090
X
X
7.4
71.0
0.413
0.115
15.3
78.7
0.542
0.124
Unseen
X
8.0
72.1
0.420
0.109
16.0
79.3
0.558
0.116
9.2
73.1
0.431
0.099
17.1
81.5
0.571
0.103
The ablation variant without SCSP shows a clear drop (-0.8% seen, -0.7% unseen aloU), confirming SCSP's role in learning transferable, multi-scale af- fordance cues. Even without it, our model still outperforms [5, 6], validating the strength of our differential geometry- based self-prompting. All methods ex- hibit performance degradation in unseen scenarios, but our model maintains the smallest drop, indicating greater robustness to distribution shifts enabled by shape-awar aaaee reasoning rather than speccic memorization.
Table 3: Cross- generalization (LASO
Seen
Unseen
Method
aloU
AUC
SIM
aloU
AUC
SIM
3DAILLM [6]
17.6
82.4
0.57
10.8
72.5
0.425
GEAL [5]
18.4
83.2
0.59
11.6
73.8
0.44
Ours w/o SCSP
18.9
83.6
0.595
11.8
74
0.445
Ours (Full Model)
19.7
84.5
0.61
12.5
75.2
0.465
Conclusion and Future Works
We presented VISPLA, geometry-aware iterative framework for gegguaee-guidee. 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 fixed-label paradigms. Experimental results LASO and PIAD benchmarks show strong generalization, particularly in zero-shot and tross-dataset settings. Despite its strengths, ViSPLA incurs additional computation due 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, 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 robust, generalizable affordance understanding in complex real-world environments paving the way for more capable and adaptable embodied agents.
Acknowledgements
The authors have been supported by the following NSF grants: IIS-2331769, CMM1-22466733, and ECCS-2025929.
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Reviews:
[{"review_id": "Sz6ZeHASJA", "reviewer": "Reviewer_FK4V", "summary": "This 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.", "questions": "1) 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "In the author's rebuttal, most of my concerns have been addressed, so I would like to keep the rating.", "rebuttal": "We thank the reviewer for their careful reading, constructive points, and recognition of our technical contributions. We appreciate that the reviewer noted the rigor and novelty of our approach, as well as our detailed qualitative and quantitative analysis. We address the raised issues below.\n### $\\textcolor{red}{\\textbf{1. Inference Cost Analysis}}$\n$\\underline{\\text{Inference Efficiency and Comparison to Prior Methods}}$:\nWe agree that inference cost is an important consideration when assessing the practicality of iterative refinement approaches. In ViSPLA, inference scales linearly with the number of refinement steps, i.e.,  total runtime ≈ backbone time + T × refinement decoder time, where T is the number of self-prompting refinement iterations (Section 4.2, Tables 2&3 in the supplementary). In practice, we found that significant accuracy gains are achieved within just T=3 steps, beyond which improvements plateau (see Figure 3 of main paper, Tables 2& 3 of supplementary), balancing cost and performance.\n\n- Concrete Cost Figures: On a single NVIDIA V100 GPU, ViSPLA completes inference for 100 LASO samples in ≈14 seconds with T=3; the cost per sample is ≈0.14s, compared to ≈0.09s/sample for the best single-pass baseline (GEAL [1]). A detailed comparison is provided in the following table.\n\n- This represents a roughly 50% increase in wall-clock time per sample than GEAL [1], but delivers up to +2.4% aIoU (“seen” split, LASO benchmark [2]) and +2.66% aIoU (“seen” split, PIAD benchmark [3]) relative improvement over state-of-the-art single-pass models.  \n\n- Much of the increased computation is due to additional forward passes through a lightweight refinement decoder, while the most costly LLM and backbone modules are only invoked once per input.\n\n|Method|Inference Steps|RuntimeperSample(ms)|aIoU(LASO-Seen)|\n|-|-|-|-|\n|ViSPLA(Ours)|3|140|22.8|\n|GEAL[1]|1|94|22.0|\n|3D-AffordanceLLM[4]|1|95|18.7|\n|LASO[2]|1|76|19.7|\n|IAGNet[3]|1|103|17.8|\n  \n$\\underline{\\text{Scalability Considerations}}$:\n-The iterative modules (IDGSP, INAFS, SCSP) are computationally modest; each adds negligible overhead compared to the initial forward pass, as differential geometry and spectral operations use efficient, GPU-parallelized routines (see Section 3.4.1–3.4.3).\n-Experiments show that reducing T to 2 or adaptively stopping early (when mask change is below threshold) retains most gains while further reducing runtime (main paper Table 2 & Supp Table 3).\n\n$\\underline{\\text{Summary}}$:\nWhile our iterative refinement introduces a moderate increase in inference time (approximately 1.5× per sample compared to our single-pass backbone 3DAffordanceLLM), it leads to substantial performance gains (~22% relative) over the 3DAffordanceLLM backbone. In practice, it offers a controllable accuracy-efficiency trade-off suitable for a variety of real-world settings, and early stopping/approximate variants can further improve real-time applicability. We will clarify these details in the revised manuscript and supplement.\n\n### $\\textcolor{red}{\\textbf{2. Rationale for Iterative Refinement Components (Section 3.3 and 3.4)}}$\n\nWe appreciate the request for further conceptual clarification regarding the necessity of multiple refinement stages (IDGSP and the iterative refinement decoder).\n\n- $\\underline{\\text{IDGSP Purpose (Section 3.3)}}$:\n\n    IDGSP introduces differential geometry-based self-prompting into the iterative loop, i.e., at each step t, we compute per-point geometric descriptors—mean curvature, Laplacian, normal derivatives—from the current mask, encoding them as a visual prompt for the next update. This step specifically encourages the correction of geometric errors (e.g., poor boundary alignment, missing protrusions) in the evolving affordance mask. This is also clarified in L74-77 & L177-179 of the main paper.\n\n- $\\underline{\\text{Iterative Refinement Decoder and Additional Modules (Section 3.4)}}$:\n\n    The multi-stage refinement decoder then acts on these geometric cues together with semantic embeddings from the LLM, enabling dynamic, cross-modality correction. Beyond IDGSP, the INAFS (implicit field regularization) ensures mask smoothness and topological integrity across iterations, and SCSP (spectral self-prompting) addresses multi-scale errors (e.g., balancing global region vs. edge refinement).\n\nIn summary, while both sections involve iterative improvements:\n\n- IDGSP focuses on geometry-aware prompt generation (how to best inform the next update).\n\n- Refinement Decoder/INAFS/SCSP focus on how to optimally utilize these prompts for mask correction, enforcing both semantic and geometric consistency at multiple scales across the update loop.\n\nAblations (main paper Table 2 and Supp Table 1) demonstrate each is independently useful, delivering cumulative performance gains.\n\n### $\\textcolor{red}{\\textbf{3. Supervision and Generalization}}$\n\nThe reviewer notes the method, like most, “relies on a supervised setting, which may limit scalability to unseen categories.” We confirm this and highlight key aspects:\n\n- $\\underline{\\text{Consistency with Literature and Previous Benchmarks}}$:\n\n    Our approach follows the standard supervised learning protocol widely adopted in recent affordance segmentation literature (e.g., 3D-AffordanceLLM [4], GEAL [1], LASO [2], IAGNet [3]). To ensure a fair and meaningful comparison, we used the established data splits, annotation protocols, and evaluation benchmarks as in prior work—thus placing ViSPLA under exactly the same generalization constraints as the current state-of-the-art. Most recent papers in open-vocabulary affordance and instruction-guided 3D reasoning use the same supervised regime for training and cross-split evaluation, and we align directly with them in both datasets and metrics.\n\n- $\\underline{\\text{Robustness and Generalization in Practice}}$:\n\n    While we acknowledge that our primary training regime is supervised, both LASO and PIAD benchmarks explicitly include dedicated \"unseen\" split—where object classes or affordance types are absent from the training set. This strictly tests a model’s out-of-distribution generalization. Moreover, as shown in Table 3 (main paper), ViSPLA achieves state-of-the-art results on these challenging \"cross-dataset generalization\" tasks, providing evidence that the model generalizes well to new objects/geometries not encountered during training.\n\n- $\\underline{\\text{Geometry-Driven Inductive Bias}}$:\n\n    Beyond annotation-based learning, ViSPLA’s core modules (IDGSP, SCSP, INAFS) introduce strong inductive biases from geometry that guides the mask refinement process. In particular, INAFS allows the refinement of affordance regions using continuous implicit fields that require no extra ground-truth, relying on self-supervised optimization (mask regularity, boundary smoothness) even where labeled examples are absent. Empirically, our ablations show these geometric priors drive cross-dataset and unseen-object gains—exactly the desired property for real-world generalization.\n\n- $\\underline{\\text{Path to Less Supervised or Unsupervised Learning}}$:\n\n    We agree that scaling beyond strong supervision remains a key challenge for the field, as also acknowledged in related work. Our framework is fully compatible with future extensions to semi-supervised, few-shot, or even unsupervised settings—e.g., by leveraging INAFS and SCSP as unsupervised regularizers or by distilling geometric cues from unlabeled scenes. We acknowledge this as an exciting avenue for subsequent research.\n\n$\\underline{\\text{In summary}}$:\n\nViSPLA directly follows the established (supervised) experimental setting used throughout the literature for transparent and fair benchmarking. The rigorous \"unseen\" splits in both datasets concretely demonstrate ViSPLA’s generalization to novel, out-of-distribution objects, and our geometric modules further enhance this robustness beyond reliance on annotation alone. We hope this clarifies both our methodological choices and the broader applicability of our approach.\n\n### $\\textcolor{red}{\\textbf{4. AFF Token vs. Decoder Output for Affordance Mask Prediction}}$\n\n$\\underline{\\text{Reason for AFF Token}}$:\nWe use the \\<AFF\\> special token as an explicit cross-modal anchor—i.e., it fuses point-cloud features and instruction tokens, maximizing alignment between the language and spatial context (Section 3.2 main).\n- The AFF token’s hidden state is extracted after cross-attention and used as a semantic+contextual query for the mask decoder. This was empirically observed (in both comparison analysis and our ablation) to outperform purely using the language decoder output, which lacks direct geometric grounding.\n\n$\\underline{\\text{Alternative Tried}}$:\nWe experimented with using language decoder outputs or sequence end tokens as mask priors, but found they led to less stable and less focused affordance localization (~4-5% drop in performance). Instructed masks tended to drift away from geometric context or fixate on irrelevant language cues, a limitation also reported in [4].\n- The gain from the AFF token is likely due to richer, cross-modal representations that help the iterative modules correct both semantic and geometric errors. We will clarify this empirical finding in the revised manuscript. \n\n\n$\\underline{\\textbf{References}}$:\n\n[1] Lu et al. Geal: Generalizable 3d affordance learning with cross-modal consistency. CVPR 2025.\n\n[2] Li et al. Laso: Language-guided affordance segmentation on 3d object. CVPR 2024.\n\n[3] Yang et al. Grounding 3d object affordance from 2d interactions in images. ICCV 2023.\n\n[4] Chu et al. 3d-affordancellm: Harnessing large language models for open-vocabulary affordance detection in 3d worlds. arXiv:2502.20041."}, {"review_id": "VlUeLFpLBt", "reviewer": "Reviewer_CTMV", "summary": "This 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.", "questions": "The 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.", "limitations": "See above.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\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.", "was_revised": true, "final_justification": "After carefully reviewing the authors' detailed responses and the new experimental results provided, I can confirm that they have convincingly addressed all of my major concerns.\n\nThe authors conducted new experiments on the curated LASO-C and PIAD-C datasets. This new analysis significantly strengthens the paper's claims of robustness.\n\nThe new simulation experiments using the GraspIt! simulator are an excellent addition. It effectively demonstrates that the improved segmentation accuracy of ViSPLA translates into tangible benefits for downstream robotics tasks.\n\nI also appreciate the authors' acknowledgement of the formatting issue with Figure 3 and their commitment to correcting it in the final version.\n\nIn summary, the authors' comprehensive rebuttal has resolved most of my reservations. The work now presents a much stronger case for its novelty, robustness, and significance. Therefore, I have raised my score.", "rebuttal": "We sincerely thank the reviewer for their thorough evaluation, insightful feedback, and for acknowledging the novelty and empirical strength of ViSPLA. Below, we respond to each point in detail, providing clarifications where needed, along with new quantitative results and expanded theoretical discussion.\n\n### $\\textcolor{red}{\\textbf{1. Presentation Issue: Figure 3 Misalignment}}$\n\nWe acknowledge and apologize for the visual misalignment and stretching in Figure 3. The affected figure resulted from a formatting issue in the LaTeX build (manual aspect ratio override). In our revised submission, Figure 3 will be corrected with:\n- Consistent aspect ratios and true-to-scale visualizations.\n- Uniform, high-resolution, and professionally formatted alignments.\n\n### $\\textcolor{red}{\\textbf{2. Failure Case Analysis (Highly Articulated or Challenging Objects)}}$\n\n$\\underline{\\text{New experiments and detailed analysis}}$:\n\nTo further assess ViSPLA’s robustness in real-world scenarios and understand the failure cases, we conducted additional targeted evaluations focusing on failure modes, particularly involving highly articulated or irregular/corrupted object geometries. This included two newly curated, challenging datasets: LASO-C [1] and PIAD-C [1].\n\n$\\underline{\\text{Observed failure patterns}}$:\n\n- Errors occur most frequently on objects with complex topology (e.g., articulated tools, objects with thin handles, or occluded regions not visible in the initial point cloud). Due to rebuttal limitations, we cannot attach any visual results here, but will provide in the revised manuscript. \n- These cases are challenging for all methods, but ViSPLA's iterative mechanism reduces “leakage” of affordance masks outside the anatomically plausible regions compared to single-pass baselines, as quantitatively compared below.\n\n$\\underline{\\text{Quantitative evidence}}$:\n\n- On a subset of 120 highly articulated LASO-C objects, ViSPLA improves average aIoU by +1.7% over GEAL [1], and for a similar highly-articulated 120 subset of PIAD-C, IoU improves by 2.1% over GEAL [1] (full details in the following table). We will add these new results and discussions in the revised version.\n\n|Dataset|Method|aIoU|AUC|SIM|MAE|\n|-|-|-|-|-|-|\n|LASO-C|GEAL[1]|15.2|77.4|0.557|0.071|\n|LASO-C|ViSPLA(ours)|16.9|79.2|0.568|0.063|\n|PIAD-C|GEAL[1]|11.4|75.1|0.579|0.076|\n|PIAD-C|ViSPLA(ours)|13.5|77.8|0.592|0.069|\n\nOur targeted evaluation on challenging subsets demonstrates that ViSPLA achieves consistent gains over baselines, but residual errors persist in specific scenarios. For example, on a subset of highly articulated LASO-C and PIAD-C objects, ViSPLA improves average aIoU by +1.7% and +2.1% over GEAL, respectively. However, qualitative review reveals that failure cases are often associated with objects or regions for which language instructions are incomplete or ambiguous (e.g., references like \"pinching area\" on tongs with spring joints, where the 3D input only sparsely resolves critical contact surfaces).\n\n$\\underline{\\text{Summary}}$:\n\nThese residual errors predominantly do not arise from model design flaws, but rather stem from two sources: $\\textbf{(1)}$ intrinsic ambiguity in the provided instructions, and $\\textbf{(2)}$ limited point cloud visibility and resolution in sparse scans. ViSPLA’s geometric self-prompting and regularization modules enhance boundary alignment and mask plausibility even under partial information. However, if the initial mask is too far from the actual geometry due to these external factors, iterative refinement may stagnate—an expected limitation in ill-posed inverse geometry and under-determined segmentation tasks. This edge-case is common to all models employing fixed-point iterative regularization under sparse, ambiguous supervision.\n\n### $\\textcolor{red}{\\textbf{3. Downstream Task Impact: Robotic Grasping and Manipulation)}}$\n\nWe acknowledge that our approach is not intended to solve the entire robotic manipulation pipeline end-to-end, nor was it designed to do so. However, we still appreciate the reviewer’s interest in downstream embodied AI tasks. In response, we have conducted a more extensive assessment of how ViSPLA's predictions affect task performance in robotic grasping and manipulation settings.\n\n$\\underline{\\text{Experimental Protocol}}$:\n- $\\textbf{Simulator \\\\& Setup}$: We use the standard GraspIt! Simulator [2] and a set of 100 diverse kitchen/household object instances, focusing on multi-graspable shapes, sampled from both LASO and PIAD.\n-  $\\textbf{Grasp Planning}$: For each method, predicted affordance masks are post-processed to generate candidate regions. The planner samples grasp poses anchored on these regions, following established robotics protocols.\n-  $\\textbf{Baseline Comparison}$: Grasp success rates are compared for ViSPLA, the best single-pass baseline (GEAL [1]), and an oracle using ground-truth affordance masks.\n\n$\\underline{\\text{Quantitative Results}}$:\n|Method|Grasping Success Rate(%)|Mean Grasp Efficiency|\n|-|-|-|\n|ViSPLA(ours)|86.5|0.74|\n|GEAL[1]|81.3|0.68|\n|Oracle(GT mask)|90.0|0.76|\n\n- Grasping Success Rate: Improvement of +5.2% over GEAL.\n- Mean Grasp Efficiency: ViSPLA achieves higher mean grasp efficiency (defined as average force-closure score within afforded regions), demonstrating that its affordance maps guide not only where to grasp, but also yield more stable grasps.\n\n$\\underline{\\text{In summary}}$: ViSPLA’s advances in segmentation translate reliably into gains in downstream tasks such as grasping, both in simulation and in principle for physical robotics.\n\n$\\underline{\\textbf{Reference}}$:\n\n[1] Lu D, Kong L, Huang T, Lee GH. Geal: Generalizable 3d affordance learning with cross-modal consistency. InProceedings of the Computer Vision and Pattern Recognition Conference 2025 (pp. 1680-1690).\n\n[2] Miller AT, Allen PK. Graspit! a versatile simulator for robotic grasping. IEEE Robotics & Automation Magazine. 2004 Dec 27;11(4):110-22."}, {"review_id": "MtZY2yimBH", "reviewer": "Reviewer_S9w9", "summary": "This 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.", "questions": "Please refer to the weakness section.", "limitations": "The authors have discussed the limitations and potential negative societal impact of their work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "Most of my concerns have been addressed during the authors' rebuttal, I'd like to keep my original rating.", "rebuttal": "We sincerely thank the reviewer for recognizing the novelty and rigor of our work, specifically highlighting the effectiveness of our iterative self-prompting mechanism and the value of rich technical contributions like implicit representation and multi-scale spectral refinement. We appreciate your acknowledgment of our strong empirical results, including state-of-the-art performance, comprehensive evaluation across seen and unseen splits, and thorough ablation analysis demonstrating the impact of each module.\n\n### $\\textcolor{red}{\\textbf{1. Efficiency and Inference-Time Comparison to Single-Stage Approaches}}$\nIn ViSPLA, inference scales linearly with the number of refinement steps, i.e.,  total runtime ≈ backbone time + T × refinement decoder time, where T is the number of self-prompting refinement iterations (Section 4.2, Tables 2&3 in the supplementary). In practice, we found that significant accuracy gains are achieved within just T=3 steps, beyond which improvements plateau (see Figure 3 of main paper, Tables 2&3 of supplementary), balancing cost and performance.\n\n$\\underline{\\text{Concrete Cost Figures}}$: \n- On a single NVIDIA V100 GPU, our proposed ViSPLA completes inference for 100 LASO samples in ≈14 seconds with T=3; the cost per sample is ≈0.14s, compared to ≈0.09s/sample for the best single-pass baseline (GEAL [1]). A detailed comparison is provided in the following table.\n- This represents a roughly 50% increase in wall-clock time per sample than GEAL [1], but delivers up to +2.4% aIoU (“seen” setting, LASO benchmark [2]) and +2.66% aIoU (“seen” setting, PIAD benchmark [3]) relative improvement over state-of-the-art single-pass models.\n- Much of the increased compute is due to additional forward passes through the lightweight refinement decoder, while the most costly LLM and backbone modules are only invoked once per input.\n\n$\\underline{\\text{Quantitative Evaluation}}$:\n\nWe fully agree that computational efficiency is critical for practical deployment. To directly address this, we provide a comprehensive comparison of ViSPLA’s inference cost versus state-of-the-art baselines, focusing on per-sample runtime and segmentation performance (aIoU) on the LASO \"seen\" benchmarks. \n|Method|Inference Steps|Runtime per Sample (ms)|aIoU|\n|-|-|-|-|\n|ViSPLA(Ours)|3|140|22.8|\n|GEAL[1]|1|94|22.0|\n|3D-AffordanceLLM[4]|1|95|18.7|\n|LASO[2]|1|76|19.7|\n|IAGNet[3]|1|103|17.8|\n\n$\\underline{\\text{Mathematical Justification}}$:\n\nUnderlying the design, the refinement process is mathematically modeled as a contractive iterative map in Sobolev space $W^{2,2}(\\Omega)$ (where $\\Omega$ is the object’s geometric domain); such mappings guarantee rapid convergence and observed gains plateau after 2-3 steps, justifying our empirical $T$ choice (refer to Figure 3 of main paper and Sec. 3.2 of supplementary).\n\n$\\underline{\\text{Scalability Considerations}}$:\n- The iterative modules (IDGSP, INAFS, SCSP) are computationally modest; each adds negligible overhead compared to the initial forward pass, as differential geometry and spectral operations use efficient, GPU-parallelized routines (see Section 3.4.1–3.4.3).\n- Experiments show that reducing T to 2 or adaptively stopping early (when mask change is below threshold) retains most gains while further reducing runtime (main paper Table 2 and Supp Table 3).  \n\n$\\underline{\\text{Summary}}$:  \nWhile our iterative refinement introduces a moderate increase in inference time (approximately 1.5× per sample compared to our single-pass backbone 3DAffordanceLLM), it leads to substantial performance gains (~22% relative) over the 3DAffordanceLLM backbone. In practice, it offers a controllable accuracy-efficiency trade-off suitable for a variety of real-world settings, and early stopping/approximate variants can further improve real-time applicability. We will clarify these details in the revised manuscript and supplement.\n\n\n### $\\textcolor{red}{\\textbf{2. Robustness of Refinement to Initial Estimation Quality}}$\n\nWe agree that a practical refinement framework must be robust to variability in upstream outputs, and we directly address this concern with new robustness experiments, systematic ablation, and theoretical justification.\n\n$\\underline{\\text{Stress Testing with Degraded Initializations}}$:\n\nTo rigorously quantify ViSPLA’s resilience, we conducted targeted experiments where the initial mask from the 3D-AffordanceLLM backbone was artificially degraded or differently initialized:\n- $\\textbf{Randomization}$: Starting from random or heavily perturbed masks (random binary, eroded, dilated), as well as from alternate backbone predictions (e.g., 3D-LLM [5], PointLLM [6]-derived masks).\n- $\\textbf{Evaluation}$: We report average aIoU, AUC, SIM and MAE on PIAD “seen” benchmark [3] before and after adding our refinement modules:\n\n|Initialization|aIoU|AUC|SIM|MAE|\n|-|-|-|-|-|\n|3DAffordanceLLM(ours)|23.1|85.8|0.660|0.089|\n|Random noise|22.0|83.4|0.652|0.095|\n|Dilated/Eroded|22.9|85.3|0.658|0.091|\n\nAs seen above, even from aggressively corrupted initializations, ViSPLA regains coherent affordance structure and recovers ~95% of its optimal performance. Unlike static decoders, performance degrades gracefully—not catastrophically—as initial mask quality is reduced.\n\n|Model|aIoU|AUC|SIM|MAE|\n|-|-|-|-|-|\n|3D-LLM[5] base|18.8|76.6|0.613|0.097|\n|3D-LLM[5]+our refinement|21.9 (+6.5%)|80.9 (+6.0%)|0.645 (+5.2%)|0.093 (-3.1%)|\n|PointLLM[6] base|19.1|75.3|0.619|0.096|\n|PointLLM[6]+our refinement|22.2 (+16.2%)|81.3 (+8.0%)|0.658 (+6.3%)|0.093 (-3.1%)|\n\nAs seen above, our refinement modules consistently deliver robust improvements in accuracy and structural alignment given different initializations from diverse LLM backbones, significantly boosting all major segmentation metrics while reliably reducing error.\n\n\n$\\underline{\\text{Summary}}$:  \nThus, ViSPLA’s multi-stage, geometry-aware refinement demonstrates high empirical robustness to the initial mask, supported by a mathematically contractive iterative structure and quantitative recovery from noise or error-prone seeds. These properties differentiate ViSPLA from earlier static refinement approaches, enabling reliable practical deployment even with imperfect backbone predictors.\n\n\n$\\underline{\\textbf{References}}$:  \n[1] Lu D, Kong L, Huang T, Lee GH. Geal: Generalizable 3d affordance learning with cross-modal consistency. InProceedings of the Computer Vision and Pattern Recognition Conference 2025 (pp. 1680-1690).  \n[2] Li Y, Zhao N, Xiao J, Feng C, Wang X, Chua TS. Laso: Language-guided affordance segmentation on 3d object. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (pp. 14251-14260).  \n[3] Yang Y, Zhai W, Luo H, Cao Y, Luo J, Zha ZJ. Grounding 3d object affordance from 2d interactions in images. InProceedings of the IEEE/CVF International Conference on Computer Vision 2023 (pp. 10905-10915).  \n[4] Chu H, Deng X, Lv Q, Chen X, Li Y, Hao J, Nie L. 3d-affordancellm: Harnessing large language models for open-vocabulary affordance detection in 3d worlds. arXiv preprint arXiv:2502.20041. 2025 Feb 27.  \n[5] Hong Y, Zhen H, Chen P, Zheng S, Du Y, Chen Z, Gan C. 3d-llm: Injecting the 3d world into large language models. Advances in Neural Information Processing Systems. 2023 Dec 15;36:20482-94.  \n[6] Xu R, Wang X, Wang T, Chen Y, Pang J, Lin D. Pointllm: Empowering large language models to understand point clouds. InEuropean Conference on Computer Vision 2024 Sep 29 (pp. 131-147). Cham: Springer Nature Switzerland."}, {"review_id": "CYDMUnaaEq", "reviewer": "Reviewer_1yYi", "summary": "This 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.", "questions": "Overall, it is an interesting paper. I hope the authors address/clarify the questions raised in the weaknesses.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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?", "was_revised": true, "final_justification": "Most of my concerns have been resolved, so I raise my rating.", "rebuttal": "We thank the reviewer for clearly identifying the technical novelty, comprehensive empirical evaluation, and methodological strengths of our work. Below, we provide additional empirical results, ablations, and theoretical justifications to fully address each concern.\n\n### $\\textcolor{red}{\\textbf{1. Theoretical Rationale and Distinctiveness}}$\n\n$\\textbf{(A) INAFS: Implicit Neural Affordance Field Supervision}$\n\nINAFS introduces a continuous implicit representation for affordance masks via neural fields, going beyond discrete pointwise classification. Theoretically, this module models the affordance mask as a real-valued field $\\Phi_\\omega: \\mathbb{R}^3 \\times \\mathbb{R}^d \\rightarrow [0,1]$ where $\\Phi_\\omega$ reflects the likelihood of affordance on the object surface.\n\n$\\underline{\\text{Mathematical Formulation}}$:\n\nINAFS optimizes the following energy: $\\mathcal{E}(\\Phi_\\omega) = \\int_{\\Omega} \\|\\nabla \\Phi_\\omega(\\mathbf{x})\\|^2  d\\mathbf{x}+ \\lambda_1 \\int_{\\partial \\Omega}( \\Phi_\\omega(\\mathbf{x}) - \\mathcal{M}(\\mathbf{x}) )^2  d\\mathbf{x}+ \\lambda_2 \\int_{\\Omega}(\\|  \\Delta \\Phi_\\omega(\\mathbf{x}) \\| - \\beta \\|\\kappa(\\mathbf{x})\\| )^2  d\\mathbf{x}$\n\nThe first term (Dirichlet energy) ensures spatial smoothness and topological regularity (penalizing spurious holes/islands), while the second enforces consistency with the evolving discrete mask $\\mathcal{M}(\\mathbf{x})$ at the boundary $\\partial \\Omega$. The third term aligns the second-order variation of the field, measured by the Laplacian $\\Delta \\Phi_\\omega$, with the Gaussian curvature $\\kappa(\\mathbf{x})$ (where $\\kappa=\\kappa_1 \\cdot \\kappa_2$), thereby promoting geometric conformity with intrinsic surface structures. The optimization is tractable in function space, and the minimizer is unique by the direct method in calculus of variations.\n\n$\\underline{\\text{Impact}}$:\n\nThe continuous field \"fills in\" missing, thin, or partially occluded affordance regions by explicit geometric interpolation, ensuring robustness where crowd-sourced annotations or point cloud visibility are sparse.\n\n$\\textbf{Please refer to Section 2.2 of supp. for more in-depth analysis.}$ \n\n$\\textbf{(B) SCSP: Spectral Convolutional Self-Prompting}$\n\nSCSP acts in the frequency domain of the mesh/point cloud:\n- The evolving affordance mask $M_t$ is projected onto the eigenbasis of the mesh Laplacian $(\\{u_i\\})$, yielding spectral coefficients.\n- A learnable spectral filter $g_\\theta(\\lambda_i)$ is applied: $\\mathcal{M}_{t+1}$ \n\n$=\\sum_{i=1}^N g_\\theta(\\lambda_i) \\alpha_i^{(t)} \\mathbf{u}_i$\n- $\\underline{\\text{Why frequency-aware convolution matters}}$:  \n    - Geometric affordances often contain spatial structure at multiple scales (e.g., handles, slots, contact patches): high-frequency components correspond to fine boundaries or textured surfaces, while low frequencies model overall patch shape.\n    - Operating in the spectral domain enables targeted enhancement or suppression of structures (e.g., denoising irregular mask patches without blurring edges).\n\n- $\\underline{\\text{Comparison to prior frequency-aware methods}}$:\n\n    - To the best of our knowledge, we are the first to propose frequency-aware affordance refinement from language-prompt using our novel Spectral Convolutional Self-Prompting (SCSP) module. Our proposed approach is NOT inspired by frequency-aware networks in grid/CNN settings and is fundamentally distinct in the following aspects:\n    - We use the discrete Laplacian spectrum of the point cloud mesh (not fixed grids or images), which captures the true underlying 3D geometry.\n    - SCSP adapts filter weights dynamically at each refinement step to handle evolving mask structure, while many prior works fix kernels per layer.\n\n$\\textbf{Please refer to Section 2.3 of supp. for more in-depth analysis.}$ \n\n$\\underline{\\text{Regarding the comment “Does it work similarly to [1]?”}}$: We believe there may have been a misunderstanding, as our Reference [1] (Gibson et al.) in the main paper does not appear to relate to frequency. It is possible that a different reference was intended in the reviewer’s comment. We would be grateful if the reviewer could kindly clarify this point, so that we can appropriately compare our work with the intended reference during the discussion period.\n\n### $\\textcolor{red}{\\textbf{2. Runtime Analysis of IDGSP and Overall Computational Cost}}$\n\nBelow is a runtime decomposition of the ViSPLA inference process—measured at T=3 iterations, on a single NVIDIA V100 GPU:\n|Operation|Runtime per Sample(ms)|% of Total|\n|-|-|-|\n|Backbone+Lang Decoder|95|67%|\n|IDGSP|3/iter.(X3 iterations)|7%|\n|INAFS|3/iter.(X3 iterations)|7%|\n|SCSP|9/iter.(X3 iterations)|19%|\n|Total ViSPLA|140|100%|\n- The iterative geometric modules collectively add ≈1.5× time versus the fastest baselines, but yield up to +2.7 aIoU improvement (see above). Empirically, benefit saturates at T=3; reducing to T=2 recovers ≈95% of the performance at ≈120ms/sample.\n- Each iteration’s cost is linear in points (GPU-accelerated) and consists of geometric derivative computation, feature fusion, and a small MLP for prompt update.\n\n$\\textbf{Please refer to Reviewer S9w9’s response for detailed runtime analysis}$\n\n### $\\textcolor{red}{\\textbf{3. Quantitative and Qualitative Evidence}}$\n\n$\\textbf{(A) Quantitative Ablations}$\n||||LASO||||PIAD|||\n|-|-|-|-|-|-|-|-|-|-|\n|Type|Setting|aIoU|AUC|SIM|MAE|aIoU|AUC|SIM|MAE|\n|Seen|Ours(full model)|22.8|87.3|0.651|0.090|23.1|85.8|0.664|0.089|\n|Seen|w/oIDGSP|20.3|86.1|0.643|0.094|22.9|84.2|0.657|0.093|\n|Seen|w/oINAFS|20.4|86.5|0.644|0.095|22.5|84.7|0.659|0.094|\n|Seen|w/oSCSP|21.3|86.7|0.649|0.093|22.7|84.0|0.660|0.092|\n|Unseen|Ours(full model)|17.1|81.5|0.571|0.103|9.2|73.1|0.431|0.099 |\n|Unseen|w/oIDGSP|16.5|80.7|0.566|0.110|8.5|72.5|0.429|0.105|\n|Unseen|w/oINAFS|16.4|80.4|0.567|0.110|8.4|72.3|0.428|0.109|\n|Unseen|w/oSCSP|16.9|80.9|0.568|0.106|8.7|72.8|0.428|0.110|\n- IDGSP is central to precise and structurally consistent mask evolution, as it enables the iterative, geometry-aware correction and regularization essential for robust generalization, especially on complex geometries.\n- Removing INAFS most affects MAE and overall mask topology, confirming its role in maintaining smoothness, connectedness, and geometric plausibility of affordance regions.\n- Dropping SCSP reduces both spectral SIM and AUC, signaling higher edge localization error and more fragmented predictions, as SCSP governs multi-scale mask communication and refinement.\n\n$\\textbf{(B) Qualitative Evaluation}$\n\nDue to limitations of rebuttal, we cannot provide figures here. Please check Figure 5 of the main paper for visualizations. \n- IDGSP: 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. \n- INAFS: In challenging cases (thin handles, small regions), masks remain connected and exhibit correct topology, even when point data is irregular or partially missing.\n- SCSP: On articulated objects, spectral updates produce masks that are less noisy, have crisper boundaries, and avoid folding or over-smoothing seen with purely spatial filters.\n\n### $\\textcolor{red}{\\textbf{4. On the Use of INAFS and SCSP as General Geometric Regularizers}}$\n\nYes, both INAFS and SCSP are designed as general-purpose geometric regularizers and can be directly integrated into the training or fine-tuning pipeline of other 3D affordance-aware LLMs, independent of the backbone architecture.\n- INAFS regularizes the predicted mask to be smooth, continuous, and topologically plausible by penalizing implausible discontinuities or spurious islands, regardless of the underlying semantic decoder.\n- SCSP enforces multi-scale consistency and preserves spectral signature of the mask, making predictions robust against both low-frequency (global shape) and high-frequency (edge/detail) noise. As SCSP operates in the Laplacian eigenbasis of the point cloud, it is naturally compatible with a wide variety of encoder-decoder architectures and supervision schemes.\n\n$\\underline{\\text{Quantitative Results: Transfer to Other 3D LLM Pipelines}}$:\n\nTo address this, we adopted two more 3D-aware LLMs (other than the 3D-AffordanceLLM in our paper): 3D-LLM [5] and PointLLM [6]—with our refinement. We evaluate on PIAD “seen” benchmark [3] before and after adding our refinement modules:\n|Model|aIoU|AUC|SIM|MAE|\n|-|-|-|-|-|\n|3D-LLM[5] base|18.8|76.6|0.613|0.097|\n|3D-LLM[5]+our refinement|21.9(+16.5%)|80.9(+6.0%)|0.645(+5.2%)|0.093(-3.1%)|\n|PointLLM[6] base|19.1|75.3|0.619|0.096|\n|PointLLM[6]+our refinement|22.2(+16.2%)|81.3(+8.0%)|0.658(+6.3%)|0.093(-3.1%)|\n\nIntegrating our geometric refinement with existing baselines yields substantial improvements: both 3D-LLM and PointLLM see performance gains exceeding 5–16% while consistently reducing MAE.\n\n$\\underline{\\text{Generalization}}$: Both modules require only point cloud geometry and the predicted mask as input—they do not rely on language features, specific model weights, or architecture assumptions. As regularizers, they can thus be flexibly attached as plug-and-play heads or losses during fine-tuning or post-processing steps for any 3D affordance-aware LLM pipeline.\n\n$\\underline{\\textbf{References}}$:\n\n[1] Lu et al. Geal: Generalizable 3d affordance learning with cross-modal consistency. CVPR 2025.\n\n[2] Li et al. Laso: Language-guided affordance segmentation on 3d object. CVPR 2024.\n\n[3] Yang et al. Grounding 3d object affordance from 2d interactions in images. ICCV 2023.\n\n[4] Chu et al. 3d-affordancellm: Harnessing large language models for open-vocabulary affordance detection in 3d worlds. arXiv:2502.20041\n\n[5] Hong et al. 3d-llm: Injecting the 3d world into large language models. NeurIPS 2023.\n\n[6] Xu et al. Pointllm: Empowering large language models to understand point clouds. ECCV 2024."}]

Author rebuttal:

Meta-review: The paper presents a language-driven 3D affordance prediction with state-of-the-art performance. Initially, the paper got mixed ratings, but after the rebuttal and author-reviewer discussion phase, all reviewers recommend acceptance, explicitly noting that all concerns have been addressed. 

Given this strong consensus of reviewers and the clear strengths of the paper, the AC recommends accepting this paper.
Decision: Accept (poster)