SuGaR
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[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
Thanks for your great work! After joint refine, I care whether there is a correspondence between all Gaussians and the mesh. If I want to edit to move part of...
python gaussian_splatting/train.py -s dog_7000.ply --iterations 7000 -m out get error: Optimizing out Output folder: out [27/04 17:19:20] Tensorboard not available: not logging progress [27/04 17:19:20] Traceback (most recent call last):...
During the joint optimization process, the vertex positions of the mesh and the Gaussian distribution parameters bound to them are simultaneously optimized? How exactly are the vertices of the mesh...
 To begin, I processed the video using Colmap with the following commands: Convert the video to individual frames: ffmpeg -i /project/gaussian-splatting/Museum.mp4 -vf fps=2 -vsync vfr -q:v...
Thanks for your great work! I got good result in garden scene, but bad result on bicycle. I tried density and sdf regularization_type, it doesn't work. What is the cause...
Hi,Thank you very much for open source such a good project。 The effect is still good after I mesh it, but it looks like there is some white content from...
 i trained with python train.py -s /home/xiangyue/joonwon/SuGaR/gaussian_splatting/data/bat/ -c /home/xiangyue/joonwon/SuGaR/gsdata/bat2/ -r density --low_poly True --postprocess_mesh True
System: `Ubuntu 22.04.4 LTS` GPU: `NVIDIA RTX 3090` Dataset: [`tandt_db`](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip) Scene: `truck` GS Iterations: `30000` Command: `python train.py -s ../../gs-repo/tandt/truck -c ./gsoutputs/tandt_truck_long/ -r sdf --refinement_time long --iteration_to_load 30000` A part...
Hi! First of all, amazing work on this project! Really excited to see the outputs it's capable of. Would it be possible to use an existing GS PLY file without...
## 1. gaussian_splatting ### 1.1 run train.py (gaussian_splatting): This should be easy: run: `python gaussian_splatting/train.py -s --iterations 7000 -m ` my for example: `python gaussian_splatting/train.py -s gaussian_splatting\sherioc_dataset\train --iterations 7000 -m...