Kati8/ICLR_2023_2025 huggingface dataset:
authors: Jiaxiang Tang
authors: Jiawei Ren
authors: Hang Zhou
authors: Ziwei Liu
authors: Gang Zeng
reviews: 
Comments: This paper proopsed a way to combine SDS loss and the recently proposed point-based rendering method GS. The pipeline is composed of 3 stages, 1) gaussian splatting optimization; 2) mesh extraction from point clouds; 3) texture refinement. The first stage is similar to other SDS-based methods which require pretrained 2d image diffusion models. However, the rendering method is switched to gaussian splatting. The second stage is done by applying marching cubes to opacities learned in the first stage. In the last stage, the texture is refined using 2D diffusion models. The full pipeline takes several minutes and we can obtain a mesh with textures. The authors show some results of image-conditioned and text-conditioned generation.
Strengths: The paper has many strengths. Thanks to GS, the full pipeline is fast compared to some previous nerf-based methods. The author observed that a longer optimization of SDS does not give better results (sharp and detailed). Thus the focus of the method is not the SDS part. Instead, the authors only optimize the SDS loss with a few iterations (which is also the main reason why it is so fast). After obtaining the blurry 3D object, a refinement step inspired by diffusion-based image editing is applied. In the end, we can have a detailed mesh with textures. Another important aspect of this method, is the mesh extraction algorithm. Extracting a surface from a point cloud is not straightforward. The authors found out a way to use the opacity as the isosurface.

When we are generating data, GS seems to be more suitable because of its progressive nature. The paper can be seen as a proof of this claim.
Weaknesses: 1. It seems the focus of the paper is image-conditioned generation. The results of text-to-3d are very limited and the comparison is weak.
2. I am curious about the setup of the stage 3. If we already have a mesh with coarse texture, we can optimize it with differentiable mesh rendering and SDS loss, as Fantasia3d did in the appearance modeling stage. What is the advantage of the proposed refinement compared to this?
3. Another concurrent ICLR submission ( https://openreview.net/forum?id=pnwh3JspxT ) optimizes SDS much longer (1 hour and 40 minutes) than this paper. However, this paper claims that longer training does not give better results. Can the authors clarify the differences?
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: The present work presents a new methodology for generative 3d modelling using a 2d lifting approach. Its main underlying hypothesis is that using 3d Gaussian Splatting with its densification results in much faster convergence compared to using traditional neural radiance fields. On the technical side, this work focuses on two main contributions. First it proposes a mesh extraction technique based on the marching cubes algorithm for the setting of environment representations using 3d gaussians. Second, it proposes a UV-space texture refinement stage for further quality enhancement of the resulting textures.
Strengths: Overall I think this paper to be interesting and to make relevant contributions. While most of the underlying ideas have already been presented in prior works, their combination for the presented use-case is relevant and results in significant performance improvement for the tasks of image-to-3d and text-to-3d.
Weaknesses: While the work has overall a good contribution, one of my main concern is its writing. First, there are numerous minor language issues such as grammar mistakes and sometimes unnecessarily complicated sentence structure. This can easily be solved by a few rounds of careful proofreading. Second, the work reads more like a paper written for a computer vision conference without providing sufficient background to a broader audience at the ICLR community. While this style is not unprecedented in ML, it makes this work much harder accessible and misses an opportunity. Moreover, for people outside the specific subarea of 3d content creation some of the important implementation details may be missing.

These concerns range from minor points such as assuming the reader to be familiar with all mentioned vision / graphics concepts such as UV space etc without providing a proper background section. It also involves more complicated points such as the decision to provide some background (e.g. on SDS loss) but only to an extent that is only meaningful for people already familiar with dreamfusion. While in vision, many of these things can be assumed known, it would be useful to the learning community to provide some information here.

Also, I would rephrase the contribution bullet points to focus stronger on the technical aspects rather than first mentioning the overall framework, then dedicating one point to the actual technical meat and then talking about experimental evaluation.
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 1 poor