7 papers
Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
Haotong Lin, Sida Peng, Jingxiao Chen +7
Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation m…
ADGaussian: Generalizable Gaussian Splatting for Autonomous Driving via Multi-modal Joint Learning
Qi Song, Chenghong Li, Haotong Lin +2
We present a novel approach, termed ADGaussian, for generalizable street scene reconstruction. The proposed method enables high-quality rendering from merely single-view input. Unl…
Split4D: Decomposed 4D Scene Reconstruction Without Video Segmentation
Yongzhen Hu, Yihui Yang, Haotong Lin +8
This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representat…
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation
Zhen Xu, Hongyu Zhou, Sida Peng +13
Depth estimation is a fundamental task in 3D computer vision, crucial for applications such as 3D reconstruction, free-viewpoint rendering, robotics, autonomous driving, and AR/VR…
StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models
Yunzhi Yan, Zhen Xu, Haotong Lin +8
This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data. Recent advancements in neural scene representation have achieved notable success in…
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
Shangzan Zhang, Sida Peng, Tianrun Chen +5
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single ima…