Search arXiv⌕ Search

arXiv · 2610.24811

Directionally Factorized Illumination with Spherical Harmonic Rotation

Abstract

Many real-world capture scenarios involve an active light source rigidly attached to the camera: camera flashes for dark-environment imaging, underwater vehicle lights, and endoscopic/laparoscopic medical procedures. As the camera moves, the co-mounted light moves with it, causing the illumination direction at each surface point to change across views. Neural rendering methods (NeRF, Gaussian Splatting) encode this pose-dependent illumination as surface coloration, producing appearance inconsistencies and making it harder to recover the underlying scene. We present \textbf{DaFISH}, which can be plugged into any novel-view synthesis (NVS) pipeline such as NeRF. It factorizes per-point emitted color into surface albedo and a spherical harmonic (SH)-represented illumination factor. During training, the SH illumination is evaluated directly, enabling stable joint optimization of albedo, lighting, and geometry. \textbf{DaFISH} provides analytical generalization of illumination to arbitrary camera poses without retraining. Building on the closed-form SH-to-irradiance conversion, the training pipeline is fully differentiable from SH evaluation through volume rendering. Averaged over the held-out frames of the five scenes, \textbf{DaFISH} attains the best mean PSNR (38.91\,dB), SSIM (0.9613), and LPIPS (0.0582) among all compared methods. It improves over the strongest splatting baseline as well as the closest NeRF-based baseline, while recovering the scene consistently across views compared to prior methods.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yimeng Dou, Adam M. Saunders, Andre T. S. Hucke, Michael E. Kim, Adam W. Anderson, Eric R. Tkaczyk, Bennett A. Landman, Daniel Moyer. 2026-10-17. Directionally Factorized Illumination with Spherical Harmonic Rotation. https://arxiv.org/abs/2610.24811

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bilateral Sharpness-Aware Minimization for Flatter Minima

Sharpness-Aware Minimization (SAM) enhances generalization by reducing a Max-Sharpness (MaxS). Despite the practical success, we empirically found that the MAxS behind SAM's generalization enhancements face the "Flatness Indicator Problem" (FIP), where SAM only considers the flatness in the direction of gradient ascent, resulting in a next minimization region that is not sufficiently flat. A better Flatness Indicator (FI) would bring a better generalization of neural networks. Because SAM is a greedy search method in nature. In this paper, we propose to utilize the difference between the training loss and the minimum loss over the neighborhood surrounding the current weight, which we denote as Min-Sharpness (MinS). By merging MaxS and MinS, we created a better FI that indicates a flatter direction during the optimization. Specially, we combine this FI with SAM into the proposed Bilateral SAM (BSAM) which finds a more flatter minimum than that of SAM. The theoretical analysis proves that BSAM converges to local minima. Extensive experiments demonstrate that BSAM offers superior generalization performance and robustness compared to vanilla SAM across various tasks, i.e., classification, transfer learning, human pose estimation, and network quantization.

cs.CV↗

Understanding Transformer-Based Vision Models via Modular Feature Inversion

Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promising yet only preliminarily explored approach is feature inversion via inverse networks, which reconstructs images from intermediate representations using trained inverse networks. In this study, we revisit feature inversion via inverse networks, introducing a novel, modular variant that enables a computationally more efficient application of the technique while, in some cases, producing semantically more coherent image reconstructions. We apply our method to large-scale transformer-based vision models, specifically Detection Transformer, Vision Transformer, Swin Transformer, and Data-Efficient Image Transformer, analyzing the resulting reconstructions across network depth. Our main analysis compares Detection Transformer and Vision Transformer, which exhibit the most informative differences among the evaluated architectures. At the same time, results for Swin Transformer and Data-Efficient Image Transformer support the broader applicability of our framework. Our findings reveal gradual representational changes across transformer layers as a shared characteristic of Detection Transformer and Vision Transformer, as well as systematic differences in how they preserve contextual shape and fine-grained image details, and in their robustness to color perturbations. These findings deepen understanding of transformer-based vision models and demonstrate the utility of modular feature inversion as an interpretability tool.

cs.CV↗

Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs

Humans develop perception through a bottom-up hierarchy: from basic primitives and Gestalt principles to high-level semantics. Current Multimodal Large Language Models (MLLMs) are trained directly on complex downstream tasks. Does it guarantee their performance on these foundational visual capabilities? To systematically investigate this gap, we introduce VisFacor, a benchmark that digitizes 20 vision-centric subtests from FRCT, a well-established cognitive psychology assessment spanning four domains of human visual cognition. Furthermore, we design algorithms to automatically construct and validate unlimited test cases with controllable difficulty. Using VisFacor, we evaluate 39 frontier MLLMs, including both proprietary (e.g., GPT, Gemini) and open-source (e.g., LLaMA, Qwen) models. The best model achieves a score of only 55.9%. Analysis reveals good internal consistency (Cronbach's alpha = 0.94) and construct validity (compared to existing vision benchmarks). Models consistently fail on tasks such as mental rotation, spatial relation inference, and figure-ground discrimination, regardless of model size or prompting strategy. These findings suggest that performance improvements on existing general benchmarks might represent castles in the air instead of a genuine mastery of human-like visual cognition.

cs.CV↗