[24] GaussianOcc: Fully Self-supervised and Efficient 3D Occupancy Estimation with Gaussian Splatting 🧑🔬 作者:Wanshui Gan, Fang Liu, Hongbin Xu, Ningkai Mo, Naoto Yokoya 🏫 单位:University of Tokyo ⟐ RI
Interactive segmentation of 3D Gaussians opens a great opportunity for real-time manipulation of 3D scenes thanks to the real-time rendering capability of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they...
In this work, we propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting. Compared with the costly neural radiance based models, 3D Gaussian Splatting has recently demonstrated great performance in terms of training time and rendering quality. However, ...
Bad result forSegmentation. Try scale the segmentation threshold, which changes the confidence score for segmentation. Missing weights for DPT. Please read thisissue Our code is based on these wonderful repos: Gaussian Splatting Wonder3D Threestudio ...
对开放词汇 3D 对象定位和语义分割的大量实验表明,LangSplat 的性能明显优于之前最先进的方法 LERF。 值得注意的是,LangSplat 非常高效,在 1440 × 1080 分辨率下,与 LERF 相比,实现了 199 × 加速。 2D-Guided 3D Gaussian Segmentation https://arxiv.org/abs/2312.16047 ...
[1] No Parameters, No Problem: 3D Gaussian Splatting without Camera Intrinsics and Extrinsics #DSPNet 探索3D场景推理问答新高度:双视觉感知网络 0.背景信息 在人工智能的前沿领域,3D场景问答(3D QA)正在成为视觉与语言理解的关键挑战。相比于传统的2D视觉问答(VQA),3D QA需要模型不仅能够感知复杂的三维空间结...
通过高效的4D Gaussian Splatting的表达,2D和3D伪标签的监督和时空的连续性约束,使得4DGen可以实现高分辨率、长时序的高质量的4D内容生成。 #O²-Recon 在计算机视觉中,物体级别的三维表面重建技术面临诸多挑战。与场景级别的重建技术不同,物体级别的三维重建需要为场景中的每个物体给出独立的三维表示,以支持细粒度...
[NeRF进展,高质量快速训练、1080P实时渲染] INRIA,MPI等推出3D Gaussian Splatting,使用3D高斯表达场景和快速可见感知渲染 05:05 [NeRF进展,自动数据收集] INSA, UCBL, Meta提出AutoNeRF,一种不需要人工干预的自动agent,采集NeRF训练数据,协助完成下游任务 01:50 [NeRF进展,物体相机] MIT与莱斯大学脑洞大开:ORC...
[NeRF进展,高质量快速训练、1080P实时渲染] INRIA,MPI等推出3D Gaussian Splatting,使用3D高斯表达场景和快速可见感知渲染 05:05 [NeRF进展,自动数据收集] INSA, UCBL, Meta提出AutoNeRF,一种不需要人工干预的自动agent,采集NeRF训练数据,协助完成下游任务 01:50 [NeRF进展,物体相机] MIT与莱斯大学脑洞大开:ORC...
Our approach effectively captures these priors by utilizing a Gaussian Splatting-based auto-decoder network with part-based dynamic modeling. Our method employs identity-shared encoding with personalized latent codes for individual identities to learn the attributes of Gaussian primitives. During the ...