Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
A Unified Framework for 3D Scene Understanding
Authors: Wei Xu, Chunsheng Shi, Sifan Tu, Xin Zhou, Dingkang Liang, Xiang Bai
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmarks, including Scan Net20, Scan Refer, and Scan Net200, demonstrate that the Uni Seg3D consistently outperforms current SOTA methods, even those specialized for individual tasks. |
| Researcher Affiliation | Academia | Wei Xu , Chunsheng Shi , Sifan Tu, Xin Zhou, Dingkang Liang, Xiang Bai Huazhong University of Science and Technology EMAIL |
| Pseudocode | No | The paper describes its methodology in Section 3 and illustrates it with Fig. 2, but it does not provide formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models are available at https://dk-liang.github.io/Uni Seg3D/. |
| Open Datasets | Yes | Datasets. We evaluate the Uni Seg3D on three benchmarks: Scan Net20 [6], Scan Refer [1], and Scan Net200 [47]. |
| Dataset Splits | Yes | All models are trained for 512 epochs on a single NVIDIA RTX 4090 GPU and evaluated per 16 epochs on the validation set to find the best-performed model. |
| Hardware Specification | Yes | All models are trained for 512 epochs on a single NVIDIA RTX 4090 GPU and evaluated per 16 epochs on the validation set to find the best-performed model. |
| Software Dependencies | No | The paper mentions using a 'frozen CLIP [46] text encoder' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | We adopt the Adam W optimizer with the polynomial schedule, setting an initial learning rate as 0.0001 and the weight decay as 0.05. All models are trained for 512 epochs on a single NVIDIA RTX 4090 GPU and evaluated per 16 epochs on the validation set to find the best-performed model. |