Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly
Authors: Junsheng Zhou, Yu-Shen Liu, Zhizhong Han
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct evaluations on various datasets, and report analysis, numerical and visual comparisons with the latest methods to show our superiority. |
| Researcher Affiliation | Academia | Junsheng Zhou1 Yu-Shen Liu1 Zhizhong Han2 School of Software, Tsinghua University, Beijing, China1 Department of Computer Science, Wayne State University, Detroit, USA2 |
| Pseudocode | No | The paper describes its methods through textual explanations and figures (e.g., Figure 2), but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide our demonstration code as a part of our supplementary materials. We will release the source code, data and instructions upon acceptance. |
| Open Datasets | Yes | We evaluate deep prior assembly under four widely-used 3D scene reconstruction benchmarks 3D-Front [17], Replica [55], Blend Swap [2] and Scan Net [11]. |
| Dataset Splits | No | The paper explicitly mentions using a "test set" for evaluations (e.g., "randomly select 1,000 scene images from the test set"), but does not specify a separate validation split or its proportion. |
| Hardware Specification | Yes | The total 1,000 iterations take 9.2 seconds on a single 3090 GPU. |
| Software Dependencies | No | The paper references specific models like Grounded-SAM [29, 33], Stable-Diffusion [51], Open-CLIP [47, 23], Shap E [26], and Omnidata [13], but does not provide specific version numbers for software dependencies or libraries (e.g., PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | The number M of samples generated by Stable-Diffusion for each instance is set to 6, where we select the Top K = 3 samples with Open-CLIP. The pose/scale optimization is repeated for r = 10 times for each instance with RANSAC-like solution. |