activity
20172026
most citedGeometrically Mappable Image Features

6 citations · 14 across the 26 of their papers we have counts for

collaborators
Showing 2026Show all

7 papers · 1 filter

cs.LG2026

iADD: Improving Alignment and Diversity in Diffusion Policy Optimization

Ashok Prasad Neupane, Saugat Adhikari, Pramish Paudel +2

Reinforcement learning based post training of diffusion models, such as Denoising Diffusion Policy Optimization (DDPO), optimizes a reverse diffusion process under a reward functio…

cs.CV2026

iSEE: Object Permanence Through Self-Supervision

Pramish Paudel, Ajad Chhatkuli, Luc Van Gool +1

Object permanence, keeping track of an object's identity and position while it is occluded, is central to video representations that track, predict and plan. Trackers that achieve…

cs.RO2026

ϕ-RIE: From Photorealistic Reconstruction to Interactive Environments

Runyi Yang, Deheng Zhang, Xiaoye Wang +6

3D Gaussian Splatting (3DGS) can reconstruct a captured scene photorealistically, but the resulting representation does not by itself support physical interaction. Robot simulation…

cs.CV2026

ConeGaussian: Anti-Aliased Gaussian Ray-Tracing for Generic Central Cameras

Deheng Zhang, Letian Shi, Runyi Yang +6

In rendering, a camera is a sampling operator that maps each finite pixel to a bundle of rays. Different camera models change the geometry of this bundle, thus making a unified and…

cs.CV2026

LangStreet: Persistent Language Fields for Anchor-Decoded Street Gaussians

Runyi Yang, Deheng Zhang, Xiaoye Wang +6

Language Gaussian fields implicitly assume that the primitive carrying semantics remains identifiable across views. This assumption breaks in scalable anchor-decoded representation…

cs.AI2026

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

Saugat Adhikari, Ashok Prasad Neupane, Pramish Paudel +2

Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably…