3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior (2003.14052v1)

Abstract: The goal of the Semantic Scene Completion (SSC) task is to simultaneously predict a completed 3D voxel representation of volumetric occupancy and semantic labels of objects in the scene from a single-view observation. Since the computational cost generally increases explosively along with the growth of voxel resolution, most current state-of-the-arts have to tailor their framework into a low-resolution representation with the sacrifice of detail prediction. Thus, voxel resolution becomes one of the crucial difficulties that lead to the performance bottleneck. In this paper, we propose to devise a new geometry-based strategy to embed depth information with low-resolution voxel representation, which could still be able to encode sufficient geometric information, e.g., room layout, object's sizes and shapes, to infer the invisible areas of the scene with well structure-preserving details. To this end, we first propose a novel 3D sketch-aware feature embedding to explicitly encode geometric information effectively and efficiently. With the 3D sketch in hand, we further devise a simple yet effective semantic scene completion framework that incorporates a light-weight 3D Sketch Hallucination module to guide the inference of occupancy and the semantic labels via a semi-supervised structure prior learning strategy. We demonstrate that our proposed geometric embedding works better than the depth feature learning from habitual SSC frameworks. Our final model surpasses state-of-the-arts consistently on three public benchmarks, which only requires 3D volumes of 60 x 36 x 60 resolution for both input and output. The code and the supplementary material will be available at https://charlesCXK.github.io.

• The paper introduces a 3D sketch-aware feature embedding that efficiently encodes geometric data to infer complete 3D scenes.
• It employs a CVAE-based 3D sketch hallucination module with a semi-supervised structure prior to overcome challenges from incomplete observations.
• Empirical results on benchmarks like NYU Depth V2 show significant improvements in SC-IoU and SSC-mIoU, highlighting the method's efficiency.

3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior: An Academic Overview

The paper "3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior" by Xiaokang Chen and colleagues presents a notable contribution in the domain of Semantic Scene Completion (SSC) tasks. The primary challenge addressed by the authors is predicting a completed 3D voxel representation in both geometric and semantic contexts from a single viewpoint, overcoming the common bottleneck of high computational costs associated with the growth of voxel resolution.

Methodological Approach

To mitigate the resolution-related computational costs, the authors propose a novel approach of using low-resolution voxel representation infused with sufficient geometric information to infer invisible areas while preserving structural fidelity. The core innovation is the development of a 3D sketch-aware feature embedding, which explicitly encodes geometric information and guides the SSC task via a semi-supervised structure prior learning strategy.

Key to this method is the introduction of a 3D Sketch Hallucination module, which leverages a Conditional Variational Autoencoder (CVAE) to infer a full 3D sketch from partial observations. This module incorporates a strategy to sample diverse and plausible 3D sketches, thus addressing the inherent ambiguity in lifting 2D/2.5D observations to full 3D geometry.

Empirical Evaluation

The proposed approach was rigorously tested against well-established benchmarks in the field, including NYU Depth V2, NYUCAD, and SUNCG datasets. The results demonstrate superior performance compared to prior state-of-the-art methods, with considerable improvements in SC-IoU and SSC-mIoU metrics. Notably, the method only requires 3D volumes with a resolution of $60 \times 36 \times 60$ for both input and output, highlighting its computational efficiency.

Technical Merits

• Geometric Embedding: The paper's introduction of 3D sketch-aware feature embedding significantly enhances geometric information encoding, providing a solid structural representation that alleviates the need for high-resolution input.
• 3D Sketch Hallucination Module: This is a pivotal component enabling the semi-supervised structure prior to guide the SSC task, which aids in generating robust predictions even with incomplete data.
• Model Performance: Not only does the model outperform existing frameworks, but it also demonstrates the viability of integrating CVAE for structure completion, which could inspire further research into generative approaches within computer vision.

Implications

The practical implications of this research extend to numerous fields such as augmented reality, robotics, and surveillance, where effective 3D scene understanding is crucial. Theoretically, the introduction of explicit geometric embeddings and use of CVAE could pave the way for more sophisticated models that balance computational efficiency with accuracy.

Speculation on Future Developments

Future developments may explore leveraging multi-view inputs or fusing additional sensory data to enhance 3D scene completion tasks. Moreover, the concept of explicit structural priors could be extended to other domains, potentially leading to new paradigms in how machine learning models perceive and reconstruct environments.

In conclusion, this paper delivers valuable insights and methodology advancements in SSC, providing a foundation for subsequent research to build upon and apply in practical contexts within AI and computer vision.