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Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
InterpretabilityRobustness3D-aware classification with conceptsSparse volumetric object representation3D consistency
Abstract:

With the rise of deep neural networks, especially in safety-critical applications, robustness and interpretability are crucial to ensure their trustworthiness. Recent advances in 3D-aware classifiers that map image features to volumetric representation of objects, rather than relying solely on 2D appearance, have greatly improved robustness on out-of-distribution (OOD) data. Such classifiers have not yet been studied from the perspective of interpretability. Meanwhile, current concept-based XAI methods often neglect OOD robustness. We aim to address both aspects with CAVE - Concept Aware Volumes for Explanations - a new direction that unifies interpretability and robustness in image classification. We design CAVE as a robust and inherently interpretable classifier that learns sparse concepts from 3D object representation. We further propose 3D Consistency (3D-C), a metric to measure spatial consistency of concepts. Unlike existing metrics that rely on human-annotated parts on images, 3D-C leverages ground-truth object meshes as a common surface to project and compare explanations across concept-based methods. CAVE achieves competitive classification performance while discovering consistent and meaningful concepts across images in various OOD settings.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

CAVE proposes a unified framework combining concept-based interpretability with 3D volumetric representations for robust image classification. The paper resides in the 'Neural 3D Object Volumes for Robustness' leaf, which contains only three papers including CAVE itself. This represents a relatively sparse research direction within the broader taxonomy of 24 papers across multiple branches. The sibling papers (NOVUM and Escaping Plato's Cave) focus primarily on robustness through volumetric modeling but do not explicitly integrate concept-based explanations, suggesting CAVE occupies a distinct niche at the intersection of interpretability and 3D-aware classification.

The taxonomy reveals that interpretability and robustness have largely evolved along separate paths. The 'Interpretability and Explainability Methods' branch develops post-hoc and ad-hoc explanation techniques but does not emphasize 3D geometric structure or OOD robustness. Meanwhile, neighboring leaves like 'Compositional Part-Based 3D Models' address occlusion robustness through part decomposition rather than learned volumetric concepts. The '2D-to-3D Lifting' and 'Multi-View Feature Aggregation' branches aggregate spatial information but typically lack inherent interpretability mechanisms. CAVE's positioning suggests it bridges these traditionally separate concerns by grounding concept learning directly in 3D object representations.

Among 25 candidates examined, none clearly refute CAVE's three core contributions. The CAVE architecture itself was compared against 5 candidates with no overlapping prior work identified. The NOV-aware Layer-wise Relevance Propagation adaptation examined 10 candidates without finding existing methods that propagate explanations through volumetric representations in this manner. The 3D Consistency metric similarly showed no clear precedent among 10 examined papers, as existing evaluation approaches rely on 2D image annotations rather than projecting explanations onto ground-truth 3D meshes. This limited search scope suggests the contributions appear novel within the examined literature, though the relatively small candidate pool leaves open the possibility of relevant work beyond the top-25 semantic matches.

Based on the examined literature, CAVE appears to introduce a genuinely new direction by unifying concept-based interpretability with volumetric robustness. The sparse population of its taxonomy leaf and absence of refuting candidates among 25 examined papers support this assessment. However, the analysis is constrained by the search scope and does not cover the full breadth of concept-based XAI or 3D vision literature, leaving room for undiscovered related work in adjacent research communities.

Taxonomy

Core-task Taxonomy Papers
24
3
Claimed Contributions
25
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: robust and interpretable image classification using 3D object representations. The field organizes around several complementary directions. 3D-Aware Classification Architectures develop neural models that explicitly incorporate volumetric or geometric structure to improve robustness, often leveraging differentiable rendering or learned 3D embeddings. Multi-View and 2D-to-3D Projection Methods focus on aggregating information across viewpoints or lifting 2D observations into 3D space, as exemplified by approaches like Lift Splat Shoot[3] that transform image features into bird's-eye-view representations. Point Cloud and 3D Sensor Data Classification tackles direct processing of depth or LiDAR inputs using specialized architectures such as 3dmfv[4]. Interpretability and Explainability Methods aim to make model decisions transparent by grounding predictions in human-understandable 3D concepts, while Specialized Applications adapt these techniques to domains ranging from autonomous driving (PRIMEDrive CoT[5]) to medical imaging (ShapeAXI[1]) and industrial inspection (Weld Seam Detection[10]). A particularly active line of work explores how neural 3D object volumes can enhance both robustness and interpretability. NOVUM[7] demonstrates that volumetric representations improve resilience to occlusions and viewpoint changes, while Escaping Platos Cave[14] investigates how 3D-aware models can generalize beyond their training distributions. Interpretable Neural Volumes[0] sits within this cluster, emphasizing the dual benefits of volumetric reasoning: not only does it provide robustness against adversarial perturbations and out-of-distribution shifts, but the explicit 3D structure also enables more transparent decision-making compared to black-box 2D classifiers. This contrasts with purely interpretability-focused efforts like Interpretable3d[2] that prioritize explainability without necessarily leveraging volumetric robustness, and with methods such as Compositional CNN Occlusion[6] that address robustness through compositional reasoning rather than full 3D reconstruction. The interplay between geometric structure, adversarial resilience, and human-interpretable explanations remains a central open question across these branches.

Claimed Contributions

CAVE: Concept-Aware Volumes for Explanations

The authors introduce CAVE, a robust and inherently interpretable image classifier that learns sparse concepts from 3D object representations using ellipsoid neural object volumes. This framework achieves both out-of-distribution robustness and interpretability by replacing dense Gaussian features with a compact dictionary of geometrically-grounded concepts.

5 retrieved papers
NOV-aware Layer-wise Relevance Propagation (LRP)

The authors modify Layer-wise Relevance Propagation to correctly handle volumetric representations such as neural object volumes in 3D-aware architectures, ensuring the relevance conservation property is maintained while enabling faithful concept attribution from predictions to input pixels.

10 retrieved papers
3D Consistency (3D-C) metric

The authors propose 3D Consistency, a new metric that measures concept spatial consistency by projecting concept attributions onto ground-truth 3D object meshes rather than relying on human-annotated object parts. This enables evaluation of whether concepts consistently map to the same semantic regions under different poses and distribution shifts.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

CAVE: Concept-Aware Volumes for Explanations

The authors introduce CAVE, a robust and inherently interpretable image classifier that learns sparse concepts from 3D object representations using ellipsoid neural object volumes. This framework achieves both out-of-distribution robustness and interpretability by replacing dense Gaussian features with a compact dictionary of geometrically-grounded concepts.

Contribution

NOV-aware Layer-wise Relevance Propagation (LRP)

The authors modify Layer-wise Relevance Propagation to correctly handle volumetric representations such as neural object volumes in 3D-aware architectures, ensuring the relevance conservation property is maintained while enabling faithful concept attribution from predictions to input pixels.

Contribution

3D Consistency (3D-C) metric

The authors propose 3D Consistency, a new metric that measures concept spatial consistency by projecting concept attributions onto ground-truth 3D object meshes rather than relying on human-annotated object parts. This enables evaluation of whether concepts consistently map to the same semantic regions under different poses and distribution shifts.

Table of Contents