Run any Skill in Manus with one click

360roam-eval

Stars1

Forks0

UpdatedMay 11, 2026 at 07:07

Evaluates the capability of neural radiance field models to perform real-time, high-fidelity novel view synthesis on large-scale indoor scenes using 360° panoramic imagery. It probes the trade-off between rendering quality, computational efficiency, and geometric awareness in complex, unbounded indoor environments. Use when the user wants to benchmark on 360Roam Dataset, or asks about evaluating this task. Reports PSNR.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Run Skill in Manus

Source

qhjqhj00

qhjqhj00/research-skills-pool

View GitHub Repository View Creator Repositories

Download

Run Skill in Manus

Related occupationsSOC

Based on SOC occupation classification

Software DevelopersComputer and Mathematical Occupations·SOC 15-1252

SKILL.md

readonly

name	360roam-eval
description	Evaluates the capability of neural radiance field models to perform real-time, high-fidelity novel view synthesis on large-scale indoor scenes using 360° panoramic imagery. It probes the trade-off between rendering quality, computational efficiency, and geometric awareness in complex, unbounded indoor environments. Use when the user wants to benchmark on 360Roam Dataset, or asks about evaluating this task. Reports PSNR.
metadata	{"skill_kind":"dataset_eval","source_arxiv":2208.02705,"bibtex_key":"huang2022360roam","confidence":"high"}

360roam-eval

360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360$^\circ$ Radiance Fields — Huajian Huang et al. (2022) (arXiv:2208.02705, 2022)

What this evaluates

Datasets

360Roam Dataset — total ?; splits: train (-1), test (-1)

Metrics

PSNR (primary) — range: dB
- Peak Signal-to-Noise Ratio in decibels (dB), calculated as 10 * log10(MAX^2 / MSE) between the rendered and ground-truth images.
SSIM — range: [0, 1]
- Structural Similarity Index Measure, evaluating luminance, contrast, and structure similarity between images.
LPIPS — range: [0, 1]
- Learned Perceptual Image Patch Similarity, measuring perceptual difference using deep network features (lower is better).
Inference Speed — range: seconds
- Wall-clock time in seconds to render a single panoramic view at 1520×760 resolution.

Input / output format

Input: 360° panoramic images (6080×3040 resolution) with known camera poses.

Output: Rendered novel-view panoramic images (1520×760 resolution).

Scoring recipe

def compute_metrics(pred, gt):
    psnr = peak_signal_noise_ratio(gt, pred)
    ssim = structural_similarity(gt, pred)
    lpips = learned_perceptual_image_patch_similarity(gt, pred)
    return psnr, ssim, lpips

def compute_speed(render_fn):
    start = time.time()
    render_fn((1520, 760))
    return time.time() - start

Common pitfalls

Evaluation images are resized to 1520×760, not the original 6080×3040 resolution.
Inference speed is measured on a single RTX2080 Ti GPU without averaging over multiple runs or accounting for warm-up time.

Evidence (verbatim from paper)

Quantitative measurements in terms of appearance similarity include Peak Signal-to-noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) [45]. We also evaluated the inference speed of a panoramic view in 1520 × 760 on a single RTX2080 Ti GPU.

Citation

@misc{huang2022360roam,
  title={360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360$^\circ$ Radiance Fields},
  author={Huajian Huang et al. (2022)},
  year={2022},
  note={arXiv:2208.02705}
}

arXiv: 2208.02705

More from this repository

same repository

ether0-chemistry-rewards

qhjqhj00/research-skills-pool

Open-weights chemistry reasoning model + verifiable reward functions from FutureHouse's ether0 (arXiv 2506.17238). Use to score model-generated chemistry outputs (SMILES validity, molecular completion, synthesis reasoning) against ground truth, or to run the open-weights ether0 model itself for chemistry reasoning. Also useful for visualizing molecules and reactions from SMILES.

2026-05-111

2m-belebele-eval

qhjqhj00/research-skills-pool

Multilingual reading comprehension across text and speech modalities. It probes a model's ability to understand spoken or written passages in 39 languages and answer multiple-choice questions based on them. Use when the user wants to benchmark on 2M-Belebele, or asks about evaluating this task. Reports accuracy.

2026-05-111

3d-2d-vl-grounding-eval

qhjqhj00/research-skills-pool

Evaluates a unified vision-language model's ability to ground natural language instructions to 3D objects and 2D regions, as well as answer 3D visual questions. It probes spatial reasoning, cross-modal alignment, and robustness to different 3D input representations (mesh-sampled vs. sensor RGB-D point clouds). Use when the user wants to benchmark on SR3D, NR3D, ScanRefer, RefCOCO, RefCOCO+, RefCOCOg, ScanQA, SQA3D, or asks about evaluating this task. Reports top-1 accuracy (Acc@25/50/75).

2026-05-111

3d-affordance-net-eval

qhjqhj00/research-skills-pool

Evaluates 3D point cloud networks on visual object affordance understanding. It probes the model's ability to predict point-wise probabilistic scores for 18 affordance classes given full, partial, or rotated 3D shapes. Use when the user wants to benchmark on 3D AffordanceNet, or asks about evaluating this task. Reports mAP.

2026-05-111

3d-face-recon-eval

qhjqhj00/research-skills-pool

Evaluates the accuracy and realism of 3D face reconstruction and generation from single 2D images. Probes shape reconstruction fidelity against ground truth meshes, texture identity preservation across novel poses, and the diversity of synthesized 3D faces. Use when the user wants to benchmark on NoW Benchmark, REALY 3D Benchmark, or asks about evaluating this task. Reports Per-vertex error (mm).

2026-05-111

3d-ids-eval

qhjqhj00/research-skills-pool

Evaluates network intrusion detection systems on identifying malicious traffic flows in IoT and general network environments. It probes the model's ability to handle severe class imbalance, dynamic graph topologies, and both known and unknown attack patterns using binary and multi-class classification tasks. Use when the user wants to benchmark on CIC-ToN-IoT, CIC-BoT-IoT, EdgeIIoT, NF-UNSW-NB15-v2, NF-CSE-CIC-IDS2018-v2, or asks about evaluating this task. Reports F1-score.

2026-05-111

name	360roam-eval
description	Evaluates the capability of neural radiance field models to perform real-time, high-fidelity novel view synthesis on large-scale indoor scenes using 360° panoramic imagery. It probes the trade-off between rendering quality, computational efficiency, and geometric awareness in complex, unbounded indoor environments. Use when the user wants to benchmark on 360Roam Dataset, or asks about evaluating this task. Reports PSNR.
metadata	{"skill_kind":"dataset_eval","source_arxiv":2208.02705,"bibtex_key":"huang2022360roam","confidence":"high"}

360roam-eval

360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360$^\circ$ Radiance Fields — Huajian Huang et al. (2022) (arXiv:2208.02705, 2022)

What this evaluates

Datasets

360Roam Dataset — total ?; splits: train (-1), test (-1)

Metrics

PSNR (primary) — range: dB
- Peak Signal-to-Noise Ratio in decibels (dB), calculated as 10 * log10(MAX^2 / MSE) between the rendered and ground-truth images.
SSIM — range: [0, 1]
- Structural Similarity Index Measure, evaluating luminance, contrast, and structure similarity between images.
LPIPS — range: [0, 1]
- Learned Perceptual Image Patch Similarity, measuring perceptual difference using deep network features (lower is better).
Inference Speed — range: seconds
- Wall-clock time in seconds to render a single panoramic view at 1520×760 resolution.

Input / output format

Input: 360° panoramic images (6080×3040 resolution) with known camera poses.

Output: Rendered novel-view panoramic images (1520×760 resolution).

Scoring recipe

def compute_metrics(pred, gt):
    psnr = peak_signal_noise_ratio(gt, pred)
    ssim = structural_similarity(gt, pred)
    lpips = learned_perceptual_image_patch_similarity(gt, pred)
    return psnr, ssim, lpips

def compute_speed(render_fn):
    start = time.time()
    render_fn((1520, 760))
    return time.time() - start

Common pitfalls

Evaluation images are resized to 1520×760, not the original 6080×3040 resolution.
Inference speed is measured on a single RTX2080 Ti GPU without averaging over multiple runs or accounting for warm-up time.

Evidence (verbatim from paper)

Quantitative measurements in terms of appearance similarity include Peak Signal-to-noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS) [45]. We also evaluated the inference speed of a panoramic view in 1520 × 760 on a single RTX2080 Ti GPU.

Citation

@misc{huang2022360roam,
  title={360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360$^\circ$ Radiance Fields},
  author={Huajian Huang et al. (2022)},
  year={2022},
  note={arXiv:2208.02705}
}

arXiv: 2208.02705