Indoor 3D object detection serves as a fundamental task in computer vision and robotics. Existing research predominantly focuses on training domain-specific optimal models for individual datasets, yet it overlooks the potential value of capturing universal geometric attributes that can substantially enhance object detection performance across diverse domains. To resolve this gap, we propose COME, a novel and effective collaborative optimization framework designed to seamlessly integrate these universal attributes while preserving the domain-specific characteristics of each dataset domain. COME is built on VoteNet and incorporates a Cross-Domain Expert Parameter Sharing Strategy (CEPSS) that draws inspiration from the Mixture of Experts (MoE) framework. Its core innovation resides in the dual-expert design of CEPSS: domain-shared experts capture universal geometric relationships across datasets, whereas domain-specific experts encode unique features for individual datasets. This separation enables the model to focus on learning both generic and domain-specialized visual cues, without mutual interference. In addition, to dynamically adapt to different domains, we design a lightweight gating network that automatically selects relevant experts, eliminating irrelevant feature interference and enhancing model specialization. Compared to standard parameter-sharing architectures, this design significantly reduces gradient conflicts during multi-domain training. We further optimize computational efficiency by implementing low-rank structures for domain-shared and domain-specific experts, thus striking a better balance between memory overhead and detection performance. Experiments show that COME achieves state-of-the-art results across benchmarks, with acceptable parameter growth, and outperforms existing multi-domain detection methods.

Recently, integrating the multitask learning (MTL) paradigm into scene coordinate regression (SCoRe) techniques has achieved significant success in visual localization tasks. However, the feature extraction ability of existing frameworks is inherently constrained by the rigid weight activation strategy, which prevents each layer from concurrently capturing scene-universal features across diverse scenes and scene-particular attributes unique to each individual scene. In addition, the straightforward network architecture further exacerbates the issue of insufficient feature representation. To address these limitations, we introduce HVLF, a holistic framework that ensures flexible identification of both scene-universal and scene-particular attributes while integrating various attention mechanisms to enhance feature representation effectively. Technically, for the first issue, HVLF proposes a soft weight activation strategy (SWAS) equipped with polyhedral convolution to concurrently optimize scene-shared and scene-specific weights within each layer, which facilitates sufficient discernment of both scene-universal features and scene-particular attributes, thereby boosting the network's capability for comprehensive scene perception. For the second issue, HVLF introduces a mixed attention perception module (MAPM) that incorporates channelwise, spatialwise, and elementwise attention mechanisms to perform multilevel feature fusion, hence extracting discriminative features to regress precise scene coordinates. Extensive experiments on indoor and outdoor datasets prove that HVLF realizes impressive localization performance. In addition, experiments conducted on 3-D object detection and feature matching tasks prove that the two proposed techniques are universal and can be seamlessly inserted into other methods.