High-Resolution Perception at Scale: Benchmarking Dense Geometric Models via Resolution-Calibrated Corruptions

While recent advancements in large-scale pretraining have significantly improved dense geometric models, these architectures often struggle to adapt to 4K-resolution inputs because of their rigidity. Existing benchmarks mostly provide low-resolution samples and lack quantitative evaluations on high-resolution corrupted inputs. We address these gaps in two ways. First, we propose the Resolution-Calibrated Corruptions (RCC) framework to dynamically scale perturbations. Second, we introduce a benchmark with over 22,000 samples to evaluate optical flow and depth estimation architectures at 1K, 2K, and 4K resolutions. Our evaluation reveals a dual challenge: natively processing 4K inputs exposes architectural rigidities, while workarounds like input downsampling or spatial tiling may introduce spatial-aliasing artifacts or lose global context. Experiments show that traditional optical flow architectures degrade considerably under dense weather occlusions at standard resolutions. When natively processing 4K inputs, spatial redundancy mitigates these sparse occlusions, but—under our simulated physical sensor constraints—exposes a noticeable vulnerability to high-frequency sensor noise and yields higher absolute errors. Consequently, while models leveraging geometric priors remain noticeably more robust to noise and scale changes, input downsampling remains the most pragmatic choice for overall accuracy, despite potentially introducing spatial aliasing artifacts.