10 papers
RefineAny3D: Depth Refinement as Semantic Alignment for Monocular 3D Detection
Zhihao Zhang, Gengwei Zhang, Tianlong Chen +1
Monocular 3D object detection spans two regimes: closed-set detectors operating within a fixed category vocabulary, and open-vocabulary detectors that localize arbitrary categories…
Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth
Jung-Hee Kim, Xiaoming Liu
Monocular depth foundation models have demonstrated remarkable generalization capabilities across diverse environments. However, they continue to struggle with metric depth estimat…
DepthAgent: Towards Better Universal Depth Estimation via Sample-wise Expert Selection
Jie Zhu, Girish Chandar Ganesan, Xiaoming Liu
Monocular metric depth estimation has achieved strong progress with large-scale training and universal-camera modeling, yet robust deployment across diverse camera settings, such a…
H-Flow: Self-supervised Human Scene Flow via Physics-inspired Joint Multi-modal Learning
Zhanbo Huang, Xiaoming Liu, Yu Kong
Parametric human models capture global pose but cannot represent the non-rigid surface dynamics of clothing and soft tissue. Generic scene flow estimates dense motion but breaks do…
UniDAC: Universal Metric Depth Estimation for Any Camera
Girish Chandar Ganesan, Yuliang Guo, Liu Ren +1
Monocular metric depth estimation (MMDE) is a core challenge in computer vision, playing a pivotal role in real-world applications that demand accurate spatial understanding. Altho…
Towards Intrinsic-Aware Monocular 3D Object Detection
Zhihao Zhang, Abhinav Kumar, Xiaoming Liu
Monocular 3D object detection (Mono3D) aims to infer object locations and dimensions in 3D space from a single RGB image. Despite recent progress, existing methods remain highly se…
EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection
Huaizhi Qu, Ruichen Zhang, Shuqing Luo +5
Recent advances in foundation models have driven remarkable progress in image editing, yet their extension to 3D editing remains underexplored. A natural approach is to replace the…
CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector
Abhinav Kumar, Yuliang Guo, Zhihao Zhang +3
Monocular 3D object detectors, while effective on data from one ego camera height, struggle with unseen or out-of-distribution camera heights. Existing methods often rely on Plucke…
Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection
Zhihao Zhang, Abhinav Kumar, Girish Chandar Ganesan +1
Monocular 3D detection (Mono3D) aims to infer 3D bounding boxes from a single RGB image. Without auxiliary sensors such as LiDAR, this task is inherently ill-posed since the 3D-to-…
RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection
Yunfei Long, Abhinav Kumar, Xiaoming Liu +1
Radar hits reflect from points on both the boundary and internal to object outlines. This results in a complex distribution of radar hits that depends on factors including object c…