13 citations · 14 across the 7 of their papers we have counts for
10 papers
Foundation4D: Towards a unified perception and reconstruction model for autonomous vehicles
NVIDIA, :, Sérgio Agostinho +25
We introduce Foundation4D, a unified feed-forward model for geometry estimation, 3D detection, semantic occupancy, and Gaussian reconstruction from multi-camera driving video. A sh…
Depth Completion as Parameter-Efficient Test-Time Adaptation
Bingxin Ke, Qunjie Zhou, Jiahui Huang +5
We introduce CAPA, a parameter-efficient test-time optimization framework that adapts pre-trained 3D foundation models (FMs) for depth completion, using sparse geometric cues. Unli…
StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
Tjark Behrens, Anton Obukhov, Bingxin Ke +3
We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without explicit depth or warpin…
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
Bingxin Ke, Kevin Qu, Tianfu Wang +5
The success of deep learning in computer vision over the past decade has hinged on large labeled datasets and strong pretrained models. In data-scarce settings, the quality of thes…
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking
Zixiang Zhao, Haowen Bai, Bingxin Ke +5
The real world is dynamic, yet most image fusion methods process static frames independently, ignoring temporal correlations in videos and leading to flickering and temporal incons…
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Massimiliano Viola, Kevin Qu, Nando Metzger +4
Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constra…
Video Depth without Video Models
Bingxin Ke, Dominik Narnhofer, Shengyu Huang +5
Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of lar…
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
Xiang Zhang, Bingxin Ke, Hayko Riemenschneider +5
By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail. Although re…
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang +3
Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not…
ImpliCity: City Modeling from Satellite Images with Deep Implicit Occupancy Fields
Corinne Stucker, Bingxin Ke, Yuanwen Yue +3
High-resolution optical satellite sensors, combined with dense stereo algorithms, have made it possible to reconstruct 3D city models from space. However, these models are, in prac…