10 papers · 1 filter
From Perspective to Fisheye Depth Estimation and Open-Vocabulary Segmentation
Rit Gangopadhyay, Alex Wong
Vision foundation models are capable of generalizing across 3-dimensional (3D) scenes with high-fidelity estimates; their empirical success can be attributed to training on large-s…
VisTa3D: A Dataset and Benchmark for Thin Object Reconstruction from Vision, Tactile, and 3D Point Clouds
Shania Guo, Yeongsik Seo, Andrew Fu +7
State-of-the-art 3D reconstruction models, whether from visual, range, or both, tend to underperform on thin objects. This is partially due to the small amount of space such object…
ETA: Energy-based Test-time Adaptation for Depth Completion
Younjoon Chung, Hyoungseob Park, Patrick Rim +7
We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when t…
Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens
Rit Gangopadhyay, Jung-Hee Kim, Xien Chen +3
We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images. Despite being trained on tens of millions of images…
Progressive Test Time Energy Adaptation for Medical Image Segmentation
Xiaoran Zhang, Byung-Woo Hong, Hyoungseob Park +5
We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is chall…
ProtoDepth: Unsupervised Continual Depth Completion with Prototypes
Patrick Rim, Hyoungseob Park, S. Gangopadhyay +3
We present ProtoDepth, a novel prototype-based approach for continual learning of unsupervised depth completion, the multimodal 3D reconstruction task of predicting dense depth map…