Optical flow estimation models are currently trained and evaluated on synthetic datasets. However, the generalizability of these models to real-world applications remains unexplored. This study investigates how well two state-of-the-art optical flow estimation models perform on real-world Articulated, Homothetic, and Conformal non-rigid motion. To facilitate evaluation, a manually annotated dataset comprising twenty-four real-world image pairs and sparse vector fields was created. Both models demonstrated performance consistent with synthetic benchmarks on Homothetic and Conformal motion. However, results degraded when evaluating Articulated motion, revealing limitations in real-world applicability for practical applications such as controlled robotics and object tracking.

Optical flow models excel on synthetic benchmarks but can struggle with real-world scenarios involving large displacements, which are critical for applications like autonomous navigation and augmented reality. To address this, we introduce a novel real-world dataset and evaluation framework, using a specialized annotation tool to capture ground truth optical flow in scenarios with fast movements and close-range objects. Our approach minimizes confounders, providing clear insights into model performance with large displacements. Findings show recent models outperform the previous state-of-the-art, RAFT, across all tested scenarios. Both the annotation tool and dataset are available to support further research.

Optical flow estimation is a core task in computer vision, yet many existing models struggle with lighting-induced appearance changes that are common in real-world scenarios. This work presents a focused evaluation of recent deep learning-based optical flow models under controlled lighting variations, using a custom dataset composed of indoor and outdoor scenes recorded with a static camera. Scenarios include glare, moving shadows, intensity shifts, and outdoor shadows, with ground truth flow defined as zero to isolate the effect of illumination changes. Four models—RAFT, GMFlow, SEA-RAFT, and FlowDiffuser—are benchmarked using standard metrics (EPE and F1-all). The results reveal that even in the absence of physical motion, several models produce significant flow estimates, particularly under shadow and intensity variation. SEA-RAFT and RAFT show relatively higher robustness, while GMFlow and FlowDiffuser are more sensitive to lighting artifacts. The findings highlight a critical gap in current model generalization and emphasize the need for lighting-aware architectures and training strategies.

Occlusions are one of the main challenges in optical flow estimation, where parts of the scene are no longer visible between consecutive frames. Several models address this problem, either intrinsically or explicitly, using different strategies. However, most benchmarks rely on synthetic data, and even real-world ones evaluate only overall model performance, without isolating occlusions. This work investigates optical flow model performance under real-world occlusions by introducing a manually annotated, occlusion-focused dataset. We present an annotation method tailored to three occlusion types: out-of-frame, inter-object, and self-occlusion. We then evaluate two models, FlowFormer++ and CCMR, which handle occlusions using different mechanisms. Our findings show that while CCMR demonstrates stronger overall performance, both models struggle with occluded regions, particularly self-occlusions involving rotation and perspective transformations. These results highlight the need for improved occlusion reasoning in models and more diverse real-world benchmarks.

It is commonly believed that image recognition based on RGB improves when using RGB-D, ie: when depth information (distance from the camera) is added. Adding depth should make models more robust to appearance variations in colors and lighting; to recognize shape and spatial relationships while allowing models to ignore irrelevant backgrounds. In this paper we investigate how robust current RGB-D models truly are to changes in appearance, depth, and background where we vary one modality (RGB or depth) and compare RGB-D to RGB-only and depth-only in a semantic segmentation setting. Experiments show that all investigated RGB-D models show some robustness to variations in color, but might severely fail for unseen variations in lighting, spatial position and backgrounds. Our results show that we need new RGB-D models that can exploit the best of both modalities while remaining robust to changes in a single modality.

Implicit neural representations (INRs) exhibit exceptional compression and generalisation abilities that have enabled striking progress across a variety of applications. These properties have fuelled a growing interest in leveraging INRs for traditional classification tasks as a memory-efficient alternative representation of images, breaking the persistent link between image resolution and associated resource costs. Current INR classification methods face limitations such as a restriction to low-resolution data and sensitivity to image-space transformations. We attribute these issues to the employed INR architecture which lacks mechanisms for local representation, thereby disregarding spatial structure within the data and furthermore limiting their ability to capture high-frequency details. In this work, we propose ARC: Anchored Representation Clouds, a novel INR architecture that explicitly anchors latent vectors in image-space. By introducing spatial structure to the latent vectors, ARC can capture local image data which in our testing leads to state-of-the-art implicit image classification of both low- and high-resolution images and increased robustness against image-space translation.