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20232026
most citedImproving Deep Regression with Ordinal Entropy

10 citations · 13 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation

Kaihua Tang, Ziqing Xia, Xiaoxu Zheng +4

Despite recent advances in Monocular Depth Estimation, state-of-the-art depth foundation models remain vulnerable to robustness issues. Particularly, even slight camera rolls can r…

cs.CV2026

AnchorSplat: Feed-Forward 3D Gaussian Splatting with 3D Geometric Priors

Xiaoxue Zhang, Xiaoxu Zheng, Yixuan Yin +5

Recent feed-forward Gaussian reconstruction models adopt a pixel-aligned formulation that maps each 2D pixel to a 3D Gaussian, entangling Gaussian representations tightly with the…

cs.CV2025

WorldWarp: Propagating 3D Geometry with Asynchronous Video Diffusion

Hanyang Kong, Xingyi Yang, Xiaoxu Zheng +1

Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, state-of-the-art gen…

cs.CV2023★ 2 cited

Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction

Ziwei Yu, Chen Li, Linlin Yang +4

Direct mesh fitting for 3D hand shape reconstruction is highly accurate. However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes. Conver…

cs.CV2023★ 10 cited

Improving Deep Regression with Ordinal Entropy

Shihao Zhang, Linlin Yang, Michael Bi Mi +2

In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance. We investigate this curious phenomenon and p…