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Yinhao Zhu

7 accepted papers

2025

ODG: Occupancy Prediction Using Dual Gaussians

NeurIPS 2025poster

Occupancy prediction infers fine-grained 3D geometry and semantics from camera images of the surrounding environment, making it a critical perception task for autonomous driving. Existing methods either adopt dense grids as scene representation which is difficult to scale to high resolution, or lear…

Cited by 0SourceScholar
2024

FutureDepth: Learning to Predict the Future Improves Video Depth Estimation

ECCV 2024poster

"In this paper, we propose a novel video depth estimation approach, , which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training. More specifically, we propose a future prediction network, F-Net, which t…

Cited by 5SourcePDFScholar
2023

Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation

ICCV 2023oral

Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene. However, it has two major limitations: path tracing is expensive to compute, and ambiguities exist between reflection and emission. Our Facto…

Cited by 14PDFcodeScholar
2023

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

NeurIPS 2023poster

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D sha…

Cited by 126SourcePDFScholar
2023

PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

CVPR 2023poster

Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part…

2019

A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images

CVPR 2019poster

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To ge…

Cited by 242PDFcodeScholar