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RESSCAL3D++: Joint Acquisition and Semantic Segmentation of 3D Point Clouds

Remco Royen, Kostas Pataridis, Ward van der Tempel, Adrian Munteanu

TL;DR

The paper tackles real-time 3D scene understanding under resolution-scalable sensing by introducing VX-S3DIS, a dataset that emulates intra-scan point-stream acquisition, and RESSCAL3D++, an enhanced method that jointly performs data acquisition and semantic segmentation. The key innovation is an update module that refines prior predictions as higher-resolution data arrives, formalized through a multi-scale fusion scheme with equations for progressive refinement. On both S3DIS and VX-S3DIS, RESSCAL3D++ achieves substantial speed-ups (15.6–63.9%) and drastically lowers scalability costs (from ~2.1% to as low as ~0.2–0.6%), while enabling the first predictions after only a small fraction of the baseline inference time (about 6–7%). This work demonstrates the practical feasibility and benefits of intra-scan processing for real-time 3D segmentation in complex indoor scenes.

Abstract

3D scene understanding is crucial for facilitating seamless interaction between digital devices and the physical world. Real-time capturing and processing of the 3D scene are essential for achieving this seamless integration. While existing approaches typically separate acquisition and processing for each frame, the advent of resolution-scalable 3D sensors offers an opportunity to overcome this paradigm and fully leverage the otherwise wasted acquisition time to initiate processing. In this study, we introduce VX-S3DIS, a novel point cloud dataset accurately simulating the behavior of a resolution-scalable 3D sensor. Additionally, we present RESSCAL3D++, an important improvement over our prior work, RESSCAL3D, by incorporating an update module and processing strategy. By applying our method to the new dataset, we practically demonstrate the potential of joint acquisition and semantic segmentation of 3D point clouds. Our resolution-scalable approach significantly reduces scalability costs from 2% to just 0.2% in mIoU while achieving impressive speed-ups of 15.6 to 63.9% compared to the non-scalable baseline. Furthermore, our scalable approach enables early predictions, with the first one occurring after only 7% of the total inference time of the baseline. The new VX-S3DIS dataset is available at https://github.com/remcoroyen/vx-s3dis.

RESSCAL3D++: Joint Acquisition and Semantic Segmentation of 3D Point Clouds

TL;DR

The paper tackles real-time 3D scene understanding under resolution-scalable sensing by introducing VX-S3DIS, a dataset that emulates intra-scan point-stream acquisition, and RESSCAL3D++, an enhanced method that jointly performs data acquisition and semantic segmentation. The key innovation is an update module that refines prior predictions as higher-resolution data arrives, formalized through a multi-scale fusion scheme with equations for progressive refinement. On both S3DIS and VX-S3DIS, RESSCAL3D++ achieves substantial speed-ups (15.6–63.9%) and drastically lowers scalability costs (from ~2.1% to as low as ~0.2–0.6%), while enabling the first predictions after only a small fraction of the baseline inference time (about 6–7%). This work demonstrates the practical feasibility and benefits of intra-scan processing for real-time 3D segmentation in complex indoor scenes.

Abstract

3D scene understanding is crucial for facilitating seamless interaction between digital devices and the physical world. Real-time capturing and processing of the 3D scene are essential for achieving this seamless integration. While existing approaches typically separate acquisition and processing for each frame, the advent of resolution-scalable 3D sensors offers an opportunity to overcome this paradigm and fully leverage the otherwise wasted acquisition time to initiate processing. In this study, we introduce VX-S3DIS, a novel point cloud dataset accurately simulating the behavior of a resolution-scalable 3D sensor. Additionally, we present RESSCAL3D++, an important improvement over our prior work, RESSCAL3D, by incorporating an update module and processing strategy. By applying our method to the new dataset, we practically demonstrate the potential of joint acquisition and semantic segmentation of 3D point clouds. Our resolution-scalable approach significantly reduces scalability costs from 2% to just 0.2% in mIoU while achieving impressive speed-ups of 15.6 to 63.9% compared to the non-scalable baseline. Furthermore, our scalable approach enables early predictions, with the first one occurring after only 7% of the total inference time of the baseline. The new VX-S3DIS dataset is available at https://github.com/remcoroyen/vx-s3dis.
Paper Structure (5 sections, 3 equations, 9 figures)

This paper contains 5 sections, 3 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Proposed dataset: VX-S3DIS
  3. Proposed method
  4. Experiments
  5. Conclusion

Figures (9)

  • Figure 1: The RESSCAL3D++ architecture, enabling joint acquisition and processing.
  • Figure 2: The data generation pipeline
  • Figure 3: A sample of the VX-S3DIS dataset. (a) The sample visualised in the complete S3DIS room (b) the sample with all points until $t=2000$ (c) the sample until $t=15000$ (d) the full resolution sample, $t=65536$. The semantic labels were employed as colors to aide the visualization
  • Figure 4: Comparison of RESSCAL3D++ with the non-scalable baseline zhao2021point and scalable state-of-the-art royen2023resscal3d on S3DIS
  • Figure 5: Comparison of RESSCAL3D++ with the non-scalable baseline zhao2021point and scalable state-of-the-art royen2023resscal3d in inference time on S3DIS. The actual inference latency is bounded to the yellow zone. The displayed non-scalable baseline timing results are not cumulative.
  • ...and 4 more figures