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Cross-Platform Benchmark of Neural 3D Reconstruction for Autonomous Laboratory Robots
Yongho Kim, Mengjiao Han, Victor Mateevitsi, Silvio Rizzi, Michael E. Papka, Nicola Ferrier
TL;DR
Autonomous laboratory robots need 3D reconstruction that meets both manipulation-quality and perception-loop latency requirements, but evidence across deployment-relevant hardware is limited. This paper benchmarks NeRF and GS across compute tiers and preliminarily assesses SAM3D, finding quality–cost and latency–fidelity trade-offs that motivate selective reconstruction strategies.
Problem
Evidence is limited on which neural reconstruction family and compute platform suit laboratory robots operating under strict perception-loop latency requirements.
Method
The study benchmarks NeRF and GS training and rendering across Jetson, desktop RTX, and A100 HPC platforms, while preliminarily evaluating SAM3D single-image reconstruction on laboratory objects.
Results
GS achieves higher PSNR and faster rendering than NeRF but uses more GPU resources, while Jetson training is substantially slower and SAM3D produces models within seconds with object-detail mismatches.
Takeaways & Limitations
Pipeline design should distinguish latency- and fidelity-oriented reconstruction and account for compute constraints when deploying neural reconstruction on laboratory robots.
Takeaways & Limitations
The work is limited to computational and reconstruction-quality benchmarking and does not develop visual analytics or integrate reconstruction into complete laboratory workflows.
Abstract
from arXiv · showhide
Autonomous robots performing laboratory tasks depend on 3D reconstruction pipelines that can turn raw camera streams into actionable object representations within the latency budget of a physical control loop. Neural 3D reconstruction methods have demonstrated high-quality view synthesis, but their real-time viability across the compute platforms on which laboratory robots actually run remains poorly characterized. In this work, we present a systematic compute-platform benchmark of neural 3D reconstruction methods, evaluating NeRF and 3D Gaussian Splatting training and rendering on GPU-enabled computing devices ranging from single-board computers to server-class nodes, and place Meta's SAM3D single-image reconstruction on the same axes to quantify its latency and fidelity gap relative to per-scene optimization. Our results show that Gaussian Splatting yields higher rendering quality than NeRF at greater GPU cost, and that onboard compute is insufficient for full per-scene optimization at interactive rates. Our preliminary assessment on SAM3D indicates that it delivers plausible object geometry within seconds, but with detail mismatches that can compromise downstream manipulation. Together, these findings motivate tiered pipelines in which lightweight feed-forward reconstruction sustains the real-time perception-and-tracking loop for laboratory robots, while heavier neural reconstruction is scheduled selectively on suitable compute.
1 INTRODUCTION
Autonomous laboratory robots require 3D representations that are both manipulation-accurate and fast enough for perception–action loops. The paper benchmarks neural reconstruction methods and compute platforms to clarify deployment trade-offs.
- 3D reconstruction and object tracking lie on the critical path between robotic sensing and action.
- NeRF and Gaussian Splatting provide high-fidelity multi-view reconstruction, whereas SAM3D offers single-image outputs in seconds with less certain fidelity.
- The study benchmarks NeRF and GS training and rendering across embedded, desktop, and HPC compute tiers, alongside a preliminary SAM3D assessment.
2 RELATED WORK
NeRF, Gaussian Splatting, and large reconstruction models occupy different positions in the latency–fidelity spectrum and impose different compute requirements. Prior work highlights both GS rendering advantages and the training and input constraints that complicate real-time robotic use.
- NeRF and GS support high-fidelity novel-view synthesis through per-scene optimization, while large reconstruction models generate object representations directly from one or a few images.
- GS uses explicit splats projected onto the image plane, enabling GPU-accelerated rendering and extensions to dynamic-scene tracking.
- GS training can take from a few minutes to over an hour and requires multi-perspective images, constraining within-a-minute reconstruction for robotic manipulation.
- Large reconstruction models predict 3D representations from single images within seconds after training on millions of 3D objects.
- Predicted unseen parts in LRM outputs may mismatch physical object geometry, while additional perspective images can reinforce the reconstruction before manipulation.
PUTE PLATFORMS
The benchmark compares NeRF and GS across embedded, desktop, and HPC platforms, revealing a quality–compute trade-off and severe onboard training slowdown.
- Cross-Platform Results: GS produces higher PSNR and faster rendering than NeRF across all evaluated devices, but requires longer training and more GPU resources.On Alienware, GS uses roughly 1.4x more GPU-minutes than NeRF.
- Quality-vs.-Cost Trade-off: 5.95 PSNR points higher: GS reaches 24.32 versus 18.96 dB on Sophia, while consuming 70% more GPU-minutes, 13.8 versus 8.1.The result quantifies GS’s fidelity advantage alongside its compute premium on the server-class node.
- Rendering Latency: GS renders faster because GPU-accelerated rasterization speeds projection onto the image plane, although the authors qualify architecture-specific benefits across devices.The paper connects this rendering advantage to the need for lightweight real-time perception and periodic heavier refinement.
- Platform Scaling: 16.5, 21.8, and 90.0 minutes: a single GS model trains on Sophia, Alienware, and Jetson Orin, respectively.Orin is 5.5x slower than the server-class platform, while only one of Sophia’s eight A100 GPUs was used.
4 PRELIMINARY RESULT OF SAM3D RECONSTRUCTION
SAM3D generates plausible single-image object reconstructions within seconds, but examples reveal geometry mismatches that can compromise manipulation and complicate fidelity evaluation.
- Inference Latency: 9–12 seconds: SAM3D generates a 3D model from a downsized 512 x 512 image on an RTX 5000 desktop GPU.Generating Gaussian splats takes 8–10 seconds, while mesh decoding finishes within a second.
- Object Reconstruction Examples: SAM3D models generally follow the input object’s shape, including plausible geometry in occluded regions, but challenging examples show imprecise reconstruction.The assessment is based on laboratory-object examples shown in Figure 5.
- Object Reconstruction Examples: The centrifuge-bucket model has more openings than the physical object, risking tube misalignment during robot manipulation.The same incorrect-opening-count problem occurs for the well plate.
- Assessing Quality: SAM3D quality comparisons face pose-matching and background-removal problems that can distort PSNR calculations.Complex scenes may leave background pixels after removal, producing false positives in the comparison region.
- Assessing Quality: On SA-3DAO, SAM3D reports an F-1 score of 0.2344 and Chamfer distance of 0.04, while the preliminary assessment raises concerns about detail mismatch for manipulation.The authors state that continuous finetuning with laboratory and unseen objects is needed.
5 REMARK AND FUTURE WORK
The paper finds a latency–fidelity balance across neural 3D reconstruction methods and compute tiers, while identifying limitations in SAM3D geometry assessment and broader system integration.
- NeRF and GS provide high-quality view synthesis but require multi-perspective images and substantial training time.
- Transformer-based models can quickly generate 3D models, but their visual and detailed geometric quality remains questionable for autonomous robot tasks.
- Benchmark scope: The benchmark provides empirical data for compute and reconstruction-quality choices, rather than proposing new reconstruction techniques or visual analytics capabilities.
- SAM3D assessment: Scale and pose mismatches between SAM3D reconstructions and ground-truth images make direct PSNR-based visual-quality comparison difficult.
- Future work: Integrating reconstruction pipelines into science-aware autonomous-manipulation systems remains future work requiring visualization, scientific context, decision support, validation, and platform integration.