TopoField is a deterministic decoder built on VOLT that addresses the field-to-instance gap in functional segmentation of indoor point clouds. It aggregates 13 test-time forward passes into a semantic consensus field, decodes instance candidates, and ranks them class‑aware, using a graph‑geodesic partition to separate high‑confidence cores from support regions. On the ECCV 2026 SceneFun3D Challenge, TopoField achieved 28.286% AP, 41.383% AP50, and 47.558% AP25, earning second place.
The paper investigates how much semantic information is lost when frozen foundation models are combined for few‑shot 3D segmentation. By varying the number of retained semantic alternatives before fusion, the authors show that keeping the full distribution of class scores yields higher harmonic‑mean IoU than collapsing to a single class. Experiments on ScanNet200 and ScanNet++ confirm that full‑distribution fusion consistently outperforms top‑1 and other operators, and that most useful information is recovered by retaining a compact set of plausible alternatives.
By Silas Kwabla Gah, Ebenezer Owusu
The paper introduces View‑Consistent Panoptic Quality (VC‑PQ), a new metric for evaluating multi‑view panoptic segmentation that treats each visible view equally and penalizes inconsistencies between predicted and ground‑truth visibility. VC‑PQ extends the traditional per‑scene PQ by decomposing the score into mask accuracy, view consistency, and matching threshold, and includes a parameter to recover area weighting for comparison. Experiments on ScanNet++ and ScanNetv2 show that VC‑PQ better reflects missed detections or ID changes across views than the conventional PQ^scene metric.
By Youngmin Lee, Byungha Ko, Guhnoo Yun, Dong Hwan Kim
VOIM (Voxel‑Grounded Online Instance Manager) is a training‑free system that builds open‑vocabulary 3D instance maps from RGB‑D or monocular RGB input by deferring label and instance decisions until sufficient soft evidence accumulates per voxel across views. Across four perception configurations on ScanNet++, VOIM outperforms the strongest online RGB‑D system, OVO‑SLAM, by 4.8–11.7 mIoU, and achieves 44.07 mIoU under a like‑for‑like protocol, winning all ten scenes. The method also runs unchanged on monocular RGB, matching baseline performance on Replica, and produces exportable occupancy grids that support free‑form instance queries.
By Sangmin Song, Sarath Kodagoda, Marc G. Carmichael, Karthick Thiyagarajan, Amal Gunatilake, Kelly Prentice, Jodi Martin
Classical image correspondence is solved at the level of sparse keypoints or dense pixels, but the systems that consume these matches - object-level mapping, topological navigation, scene-graph maintenance - reason about whole objects. Recent work narrows this gap by matchng directly at the level of instance segments: a class-agnostic segmenter partitions each image, and per-segment descriptors are obtained by pooling features from large 3D foundation models over the masks.
arXiv:2608. 05774v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce.
By Zihan Zhou, Qifu Wen, Xi Zeng