The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …

A common approach to transfer learning under distribution shift is to fine-tune the last few
layers of a pre-trained model, preserving learned features while also adapting to the new …

Depth estimation from monocular images is pivotal for real-world visual perception systems.
While current learning-based depth estimation models train and test on meticulously curated …

Current machine learning models for vision are often highly specialized and limited to a
single modality and task. In contrast, recent large language models exhibit a wide range of …

Intrinsic images, in the original sense, are image-like maps of scene properties like depth,
normal, albedo, or shading. This paper demonstrates that StyleGAN can easily be induced …

Test-time adaptation (TTA) is the problem of updating a pre-trained source model at
inference time given test input (s) from a different target domain. Most existing TTA …

Reconstructing 3D objects from a single image guided by pretrained diffusion models has
demonstrated promising outcomes. However, due to utilizing the case-agnostic rigid …

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes
too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable …

Despite the growing demand for accurate surface normal estimation models existing
methods use general-purpose dense prediction models adopting the same inductive biases …