SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks (2105.03582v2)

Abstract: Surface reconstruction from point clouds is a fundamental problem in the computer vision and graphics community. Recent state-of-the-arts solve this problem by individually optimizing each local implicit field during inference. Without considering the geometric relationships between local fields, they typically require accurate normals to avoid the sign conflict problem in overlapped regions of local fields, which severely limits their applicability to raw scans where surface normals could be unavailable. Although SAL breaks this limitation via sign-agnostic learning, further works still need to explore how to extend this technique for local shape modeling. To this end, we propose to learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks, to simultaneously achieve advanced scalability to large-scale scenes, generality to novel shapes, and applicability to raw scans in a unified framework. Concretely, we achieve this goal by a simple yet effective design, which further optimizes the pre-trained occupancy prediction networks with an unsigned cross-entropy loss during inference. The learning of occupancy fields is conditioned on convolutional features from an hourglass network architecture. Extensive experimental comparisons with previous state-of-the-arts on both object-level and scene-level datasets demonstrate the superior accuracy of our approach for surface reconstruction from un-orientated point clouds. The code is available at https://github.com/tangjiapeng/SA-ConvONet.

Analysis of SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks

In the domain of computer vision, the challenge of surface reconstruction from point clouds has consistently engaged researchers due to its critical applications ranging from computer-aided design to robotics. The paper “SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks” presents a novel framework designed to address several limitations encountered in existing methods, particularly concerning the reconstruction of surfaces from un-oriented point clouds.

The authors begin by acknowledging that traditional methods for implicit surface reconstruction often require accurate surface normals to circumvent sign conflicts in the overlapping regions of local fields. However, surface normals may not always be available, particularly in raw scans gathered via inexpensive commodity devices like Microsoft Kinect. While previous work, such as Sign-Agnostic Learning (SAL), attempted to reform this dependency by omitting surface normals, the resulting methods did not adequately account for local shape modeling.

SA-ConvONet aims to resolve these limitations by advancing the sign-agnostic learning paradigm through the integration of convolutional occupancy networks. This integration enables a unified approach that scales proficiently across large-scale scenes, offers generalization to novel shapes, and remains applicable to raw scans without requiring surface normals. The method operates by pre-training occupancy networks with convolutional features from an hourglass network architecture, which are then further optimized during inference using an unsigned cross-entropy loss.

Experimental evaluations conducted on both object-level and scene-level datasets, such as ShapeNet and ScanNet, affirm the superiority of SA-ConvONet relative to existing methodologies. Noteworthy is the method’s ability to recover fine geometric details, such as small holes and thin structures, which other state-of-the-art strategies struggle to capture. Quantitatively, the paper reports significant improvements in terms of Chamfer Distance and Normal Consistency, corroborating the qualitative observations.

From a practical perspective, the SA-ConvONet framework could substantially enhance applications where surface reconstruction is key, such as digital content creation and heritage preservation. Theoretically, the method paves the way for future explorations into sign-agnostic surface reconstruction, hinting at possibilities where additional geometric or contextual data could enhance the finesse of surface detail recovery without relying on conventional normals.

Looking ahead, while the proposed framework demonstrates compelling improvements, the computational overhead associated with test-time optimization remains a notable challenge. Therefore, future work could investigate optimization strategies or architecture alterations that maintain performance metrics yet reduce inference time.

In conclusion, SA-ConvONet marks a significant incremental step in the field of surface reconstruction, chiefly through its novel handling of sign-agnostic learning within convolutional occupancy networks and its resulting applications in large-scale, complex environments without the prerequisite of oriented normals.