activity
20232026
most citedDepth Anything: Unleashing the Power of Large-Scale Unlabeled Data

23 citations · 83 across the 27 of their papers we have counts for

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Showing 2024 · cs.CVShow all

6 papers · 2 filters

cs.CV2024

Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation

Xiaoyang Wu, Xiang Xu, Lingdong Kong +5

In this technical report, we detail our first-place solution for the 2024 Waymo Open Dataset Challenge's semantic segmentation track. We significantly enhanced the performance of P…

cs.CV2024

Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images

Zhangyang Qi, Yunhan Yang, Mengchen Zhang +7

Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, deta…

cs.CV2024★ 2 cited

Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting

Zheng Zhang, Wenbo Hu, Yixing Lao +2

3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance. However, it relies heavily on the quality of…

cs.CV2024★ 1 cited

OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding

Francis Engelmann, Ayca Takmaz, Jonas Schult +25

This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023. The goal of this wo…

cs.CV2024★ 1 cited

Towards Unified 3D Object Detection via Algorithm and Data Unification

Zhuoling Li, Xiaogang Xu, SerNam Lim +1

Realizing unified 3D object detection, including both indoor and outdoor scenes, holds great importance in applications like robot navigation. However, involving various scenarios…

cs.CV2024★ 23 cited

Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Lihe Yang, Bingyi Kang, Zilong Huang +3

This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, we aim to build a simple yet powerfu…