NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization

Zhen Li, Weiwei Sun, Shrisudhan Govindarajan, Shaobo Xia, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

Abstract: We present a novel approach to large-scale point cloud surface reconstruction by developing an efficient framework that converts an irregular point cloud into a signed distance field (SDF). Our backbone builds upon recent transformer- based architectures (i.e. PointTransformerV3), that serial- izes the point cloud into a locality-preserving sequence of tokens. We efficiently predict t he SDF value at a point by ag- gregating nearby tokens, where fast approximate neighbors can be retrieved thanks to the serialization. We serialize the point cloud at different levels/scales, and non-linearly aggregate a feature to predict the SDF value. We show that aggregating across multiple scales is critical to over- come the approximations introduced by the serialization (i.e. false negatives in the neighborhood). Our frameworks sets the new state-of-the-art in terms of accuracy and effi- ciency (better or similar performance with half the latency of the best prior method, coupled with a simpler implemen- tation), particularly on outdoor datasets where sparse-grid methods have shown limited performance.