This paper focuses on improving the reconstruction of 2D floorplans from unstructured 3D point clouds. We identify opportunities for enhancement over the existing methods in three main areas: semantic quality, efficient representation, and local geometric details. To address these, we presents SLIBO-Net, an innovative approach to reconstructing 2D floorplans from unstructured 3D point clouds. We propose a novel transformer-based architecture that employs an efficient floorplan representation, providing improved room shape supervision and allowing for manageable token numbers. By incorporating geometric priors as a regularization mechanism and post-processing step, we enhance the capture of local geometric details. We also propose a scale-independent evaluation metric, correcting the discrepancy in error treatment between varying floorplan sizes. Our approach notably achieves a new state-of-the-art on the Structured3D dataset. The resultant floorplans exhibit enhanced semantic plausibility, substantially improving the overall quality and realism of the reconstructions. Our code and dataset are available online1.

To address this challenge, we propose the framework Lambda for dangling detection and entity alignment.

We compare our method with four competing methods in Table 1 of the main paper. We also use the score reported by [5]. We found that the corner-based methods, e.g., HEA T and RoomFormer, fail to reconstruct the correct floorplans and are easily affected by the irregular Heat: Holistic edge attention transformer for structured reconstruction. Real-world perception for embodied agents.

There are several GAN literature that adopts multiple discriminators [9-14]. Among them, GMAN [10] is closely related to our approach in the sense that it utilizes an ensemble predictionofdiscriminators.

In our experiment, we use four WPA modules, two of which are in the early encoding stage and theother twoareinthelateencoding stage.

We summarize the results in Table 1. In ranking literature, many evaluation metrics are often stated in terms of gain functions. When relevance scores are restricted to be binary (i.e. Before we do so, we need some more notation regarding F . By Proposition C.1, this implies that In this section, we prove Theorem 4.2 which characterizes the agnostic P AC learnability of an arbitrary hypothesis class We begin with Lemma C.2 which asserts that if for all ERM is an agnostic P AC learner for H w.r.t ` The proof of Lemma C.2 is similar to the proof of Lemma 4.3 and involves bounding the empirical Proposition C.1, this will imply that By Proposition C.1, this implies that Next, Lemma C.3 extends the learnability of The proof of Lemma C.3 follows the same the exact same strategy used in proving Lemma 4.4.